Autonomous Vehicle Operational Management Including Operating A Partially Observable Markov Decision Process Model Instance

ABSTRACT

Autonomous vehicle operational management may include traversing, by an autonomous vehicle, a vehicle transportation network. Traversing the vehicle transportation network may include operating a scenario-specific operational control evaluation module instance, wherein the scenario-specific operational control evaluation module instance is an instance of a scenario-specific operational control evaluation module, wherein the scenario-specific operational control evaluation module implements a partially observable Markov decision process. Traversing the vehicle transportation network may include receiving a candidate vehicle control action from the scenario-specific operational control evaluation module instance, and traversing a portion of the vehicle transportation network based on the candidate vehicle control action.

TECHNICAL FIELD

This disclosure relates to autonomous vehicle operational management andautonomous driving.

BACKGROUND

A vehicle, such as an autonomous vehicle, may traverse a portion of avehicle transportation network. Traversing the portion of the vehicletransportation network may include generating or capturing, such as by asensor of the vehicle, data, such as data representing an operationalenvironment, or a portion thereof, of the vehicle. Accordingly, asystem, method, and apparatus for autonomous vehicle operationalmanagement including operating a Partially Observable Markov DecisionProcess model instance may be advantageous.

SUMMARY

Disclosed herein are aspects, features, elements, implementations, andembodiments of autonomous vehicle operational management includingoperating a Partially Observable Markov Decision Process model instance.

An aspect of the disclosed embodiments is a method for use in traversinga vehicle transportation network, which may include traversing, by anautonomous vehicle, a vehicle transportation network, wherein traversingthe vehicle transportation network includes operating ascenario-specific operational control evaluation module instance,wherein the scenario-specific operational control evaluation moduleinstance is an instance of a scenario-specific operational controlevaluation module, wherein the scenario-specific operational controlevaluation module implements a partially observable Markov decisionprocess, receiving a candidate vehicle control action from thescenario-specific operational control evaluation module instance, andtraversing a portion of the vehicle transportation network based on thecandidate vehicle control action.

Another aspect of the disclosed embodiments is a method for use intraversing a vehicle transportation network, which may includetraversing, by an autonomous vehicle, a vehicle transportation network,wherein traversing the vehicle transportation network includesinstantiating a scenario-specific operational control evaluation moduleinstance that is an instance of a scenario-specific operational controlevaluation module that implements a partially observable Markov decisionprocess modeling a distinct vehicle operational control scenario.Modeling the distinct vehicle operational control scenario may includeidentifying a plurality of states corresponding to the distinct vehicleoperational control scenario, identifying a plurality of availablevehicle control actions based on the distinct vehicle operationalcontrol scenario, identifying a plurality of conditional transitionprobabilities, wherein each conditional transition probability from theplurality of conditional transition probabilities represents aprobability of transitioning from a first respective state from theplurality of states to a second respective state from the plurality ofstates, identifying a reward function, wherein the reward functiongenerates a reward corresponding to transitioning from a firstrespective state from the plurality of states to a second respectivestate from the plurality of states, identifying a plurality ofobservations, each observation from the plurality of observationscorresponding to a respective state from the plurality of states, andidentifying a plurality of conditional observation probabilities,wherein each conditional observation probability from the plurality ofconditional observation probabilities indicates a probability ofaccuracy for a respective observation from the plurality ofobservations. Traversing the vehicle transportation network may includereceiving a candidate vehicle control action from the scenario-specificoperational control evaluation module instance, and traversing a portionof the vehicle transportation network based on the candidate vehiclecontrol action.

Another aspect of the disclosed embodiments is an autonomous vehicle forautonomous vehicle operational management including operating aPartially Observable Markov Decision Process model instance. Theautonomous vehicle may include a processor configured to executeinstructions stored on a non-transitory computer readable medium tooperate a scenario-specific operational control evaluation moduleinstance, wherein the scenario-specific operational control evaluationmodule instance is an instance of a scenario-specific operationalcontrol evaluation module, wherein the scenario-specific operationalcontrol evaluation module implements a partially observable Markovdecision process, receive a candidate vehicle control action from thescenario-specific operational control evaluation module instance, andcontrol the autonomous vehicle to traverse a portion of the vehicletransportation network based on the candidate vehicle control action.

Variations in these and other aspects, features, elements,implementations, and embodiments of the methods, apparatus, procedures,and algorithms disclosed herein are described in further detailhereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects of the methods and apparatuses disclosed herein willbecome more apparent by referring to the examples provided in thefollowing description and drawings in which:

FIG. 1 is a diagram of an example of a vehicle in which the aspects,features, and elements disclosed herein may be implemented;

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented;

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure;

FIG. 4 is a diagram of an example of an autonomous vehicle operationalmanagement system in accordance with embodiments of this disclosure;

FIG. 5 is a flow diagram of an example of an autonomous vehicleoperational management in accordance with embodiments of thisdisclosure;

FIG. 6 is a diagram of an example of a blocking scene in accordance withembodiments of this disclosure;

FIG. 7 is a diagram of an example of a pedestrian scene includingpedestrian scenarios in accordance with embodiments of this disclosure;

FIG. 8 is a diagram of an example of an intersection scene includingintersection scenarios in accordance with embodiments of thisdisclosure; and

FIG. 9 is a diagram of an example of a lane change scene including alane change scenario in accordance with embodiments of this disclosure.

DETAILED DESCRIPTION

A vehicle, such as an autonomous vehicle, or a semi-autonomous vehicle,may traverse a portion of a vehicle transportation network. The vehiclemay include one or more sensors and traversing the vehicletransportation network may include the sensors generating or capturingsensor data, such as data corresponding to an operational environment ofthe vehicle, or a portion thereof. For example, the sensor data mayinclude information corresponding to one or more external objects, suchas pedestrians, remote vehicles, other objects within the vehicleoperational environment, vehicle transportation network geometry, or acombination thereof.

The autonomous vehicle may include an autonomous vehicle operationalmanagement system, which may include one or more operational environmentmonitors that may process operational environment information, such asthe sensor data, for the autonomous vehicle. The operational environmentmonitors may include a blocking monitor that may determine probabilityof availability information for portions of the vehicle transportationnetwork spatiotemporally proximate to the autonomous vehicle.

The autonomous vehicle operational management system may include anautonomous vehicle operational management controller, or executor, whichmay detect one or more operational scenarios, such as pedestrianscenarios, intersection scenarios, lane change scenarios, or any othervehicle operational scenario or combination of vehicle operationalscenarios, corresponding to the external objects.

The autonomous vehicle operational management system may include one ormore scenario-specific operational control evaluation modules. Eachscenario-specific operational control evaluation module may be a model,such as a Partially Observable Markov Decision Process (POMDP) model, ofa respective operational scenario. The autonomous vehicle operationalmanagement controller may instantiate respective instances of thescenario-specific operational control evaluation modules in response todetecting the corresponding operational scenarios.

The autonomous vehicle operational management controller may receivecandidate vehicle control actions from respective instantiatedscenario-specific operational control evaluation module instances, mayidentify a vehicle control action from the candidate vehicle controlactions, and may control the autonomous vehicle to traverse a portion ofthe vehicle transportation network according to the identified vehiclecontrol action.

Although described herein with reference to an autonomous vehicle, themethods and apparatus described herein may be implemented in any vehiclecapable of autonomous or semi-autonomous operation. Although describedwith reference to a vehicle transportation network, the method andapparatus described herein may include the autonomous vehicle operatingin any area navigable by the vehicle.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more Application Specific Integrated Circuits, one ormore Application Specific Standard Products; one or more FieldProgrammable Gate Arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein. Insome embodiments, instructions, or a portion thereof, may be implementedas a special purpose processor, or circuitry, that may includespecialized hardware for carrying out any of the methods, algorithms,aspects, or combinations thereof, as described herein. In someimplementations, portions of the instructions may be distributed acrossmultiple processors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “example”, “embodiment”,“implementation”, “aspect”, “feature”, or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “determine” and “identify”, or anyvariations thereof, includes selecting, ascertaining, computing, lookingup, receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown and described herein.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or”. That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

FIG. 1 is a diagram of an example of a vehicle in which the aspects,features, and elements disclosed herein may be implemented. In someembodiments, a vehicle 1000 may include a chassis 1100, a powertrain1200, a controller 1300, wheels 1400, or any other element orcombination of elements of a vehicle. Although the vehicle 1000 is shownas including four wheels 1400 for simplicity, any other propulsiondevice or devices, such as a propeller or tread, may be used. In FIG. 1,the lines interconnecting elements, such as the powertrain 1200, thecontroller 1300, and the wheels 1400, indicate that information, such asdata or control signals, power, such as electrical power or torque, orboth information and power, may be communicated between the respectiveelements. For example, the controller 1300 may receive power from thepowertrain 1200 and may communicate with the powertrain 1200, the wheels1400, or both, to control the vehicle 1000, which may includeaccelerating, decelerating, steering, or otherwise controlling thevehicle 1000.

The powertrain 1200 may include a power source 1210, a transmission1220, a steering unit 1230, an actuator 1240, or any other element orcombination of elements of a powertrain, such as a suspension, a driveshaft, axles, or an exhaust system. Although shown separately, thewheels 1400 may be included in the powertrain 1200.

The power source 1210 may include an engine, a battery, or a combinationthereof. The power source 1210 may be any device or combination ofdevices operative to provide energy, such as electrical energy, thermalenergy, or kinetic energy. For example, the power source 1210 mayinclude an engine, such as an internal combustion engine, an electricmotor, or a combination of an internal combustion engine and an electricmotor, and may be operative to provide kinetic energy as a motive forceto one or more of the wheels 1400. In some embodiments, the power source1210 may include a potential energy unit, such as one or more dry cellbatteries, such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickelmetal hydride (NiMH), lithium-ion (Li-ion); solar cells; fuel cells; orany other device capable of providing energy.

The transmission 1220 may receive energy, such as kinetic energy, fromthe power source 1210, and may transmit the energy to the wheels 1400 toprovide a motive force. The transmission 1220 may be controlled by thecontroller 1300 the actuator 1240 or both. The steering unit 1230 may becontrolled by the controller 1300 the actuator 1240 or both and maycontrol the wheels 1400 to steer the vehicle. The actuator 1240 mayreceive signals from the controller 1300 and may actuate or control thepower source 1210, the transmission 1220, the steering unit 1230, or anycombination thereof to operate the vehicle 1000.

In some embodiments, the controller 1300 may include a location unit1310, an electronic communication unit 1320, a processor 1330, a memory1340, a user interface 1350, a sensor 1360, an electronic communicationinterface 1370, or any combination thereof. Although shown as a singleunit, any one or more elements of the controller 1300 may be integratedinto any number of separate physical units. For example, the userinterface 1350 and processor 1330 may be integrated in a first physicalunit and the memory 1340 may be integrated in a second physical unit.Although not shown in FIG. 1, the controller 1300 may include a powersource, such as a battery. Although shown as separate elements, thelocation unit 1310, the electronic communication unit 1320, theprocessor 1330, the memory 1340, the user interface 1350, the sensor1360, the electronic communication interface 1370, or any combinationthereof may be integrated in one or more electronic units, circuits, orchips.

In some embodiments, the processor 1330 may include any device orcombination of devices capable of manipulating or processing a signal orother information now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 1330 may include one or more specialpurpose processors, one or more digital signal processors, one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, one or more state machines, or any combination thereof. Theprocessor 1330 may be operatively coupled with the location unit 1310,the memory 1340, the electronic communication interface 1370, theelectronic communication unit 1320, the user interface 1350, the sensor1360, the powertrain 1200, or any combination thereof. For example, theprocessor may be operatively coupled with the memory 1340 via acommunication bus 1380.

The memory 1340 may include any tangible non-transitory computer-usableor computer-readable medium, capable of, for example, containing,storing, communicating, or transporting machine readable instructions,or any information associated therewith, for use by or in connectionwith the processor 1330. The memory 1340 may be, for example, one ormore solid state drives, one or more memory cards, one or more removablemedia, one or more read-only memories, one or more random accessmemories, one or more disks, including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, or any type of non-transitorymedia suitable for storing electronic information, or any combinationthereof.

The communication interface 1370 may be a wireless antenna, as shown, awired communication port, an optical communication port, or any otherwired or wireless unit capable of interfacing with a wired or wirelesselectronic communication medium 1500. Although FIG. 1 shows thecommunication interface 1370 communicating via a single communicationlink, a communication interface may be configured to communicate viamultiple communication links. Although FIG. 1 shows a singlecommunication interface 1370, a vehicle may include any number ofcommunication interfaces.

The communication unit 1320 may be configured to transmit or receivesignals via a wired or wireless electronic communication medium 1500,such as via the communication interface 1370. Although not explicitlyshown in FIG. 1, the communication unit 1320 may be configured totransmit, receive, or both via any wired or wireless communicationmedium, such as radio frequency (RF), ultraviolet (UV), visible light,fiber optic, wireline, or a combination thereof. Although FIG. 1 shows asingle communication unit 1320 and a single communication interface1370, any number of communication units and any number of communicationinterfaces may be used. In some embodiments, the communication unit 1320may include a dedicated short range communications (DSRC) unit, anon-board unit (OBU), or a combination thereof.

The location unit 1310 may determine geolocation information, such aslongitude, latitude, elevation, direction of travel, or speed, of thevehicle 1000. For example, the location unit may include a globalpositioning system (GPS) unit, such as a Wide Area Augmentation System(WAAS) enabled National Marine -Electronics Association (NMEA) unit, aradio triangulation unit, or a combination thereof. The location unit1310 can be used to obtain information that represents, for example, acurrent heading of the vehicle 1000, a current position of the vehicle1000 in two or three dimensions, a current angular orientation of thevehicle 1000, or a combination thereof.

The user interface 1350 may include any unit capable of interfacing witha person, such as a virtual or physical keypad, a touchpad, a display, atouch display, a heads-up display, a virtual display, an augmentedreality display, a haptic display, a feature tracking device, such as aneye-tracking device, a speaker, a microphone, a video camera, a sensor,a printer, or any combination thereof. The user interface 1350 may beoperatively coupled with the processor 1330, as shown, or with any otherelement of the controller 1300. Although shown as a single unit, theuser interface 1350 may include one or more physical units. For example,the user interface 1350 may include an audio interface for performingaudio communication with a person, and a touch display for performingvisual and touch-based communication with the person. In someembodiments, the user interface 1350 may include multiple displays, suchas multiple physically separate units, multiple defined portions withina single physical unit, or a combination thereof.

The sensor 1360 may include one or more sensors, such as an array ofsensors, which may be operable to provide information that may be usedto control the vehicle. The sensors 1360 may provide informationregarding current operating characteristics of the vehicle. The sensors1360 can include, for example, a speed sensor, acceleration sensors, asteering angle sensor, traction-related sensors, braking-relatedsensors, steering wheel position sensors, eye tracking sensors, seatingposition sensors, or any sensor, or combination of sensors, that isoperable to report information regarding some aspect of the currentdynamic situation of the vehicle 1000.

In some embodiments, the sensors 1360 may include sensors that areoperable to obtain information regarding the physical environmentsurrounding the vehicle 1000. For example, one or more sensors maydetect road geometry and obstacles, such as fixed obstacles, vehicles,and pedestrians. In some embodiments, the sensors 1360 can be or includeone or more video cameras, laser-sensing systems, infrared-sensingsystems, acoustic-sensing systems, or any other suitable type ofon-vehicle environmental sensing device, or combination of devices, nowknown or later developed. In some embodiments, the sensors 1360 and thelocation unit 1310 may be combined.

Although not shown separately, in some embodiments, the vehicle 1000 mayinclude a trajectory controller. For example, the controller 1300 mayinclude the trajectory controller. The trajectory controller may beoperable to obtain information describing a current state of the vehicle1000 and a route planned for the vehicle 1000, and, based on thisinformation, to determine and optimize a trajectory for the vehicle1000. In some embodiments, the trajectory controller may output signalsoperable to control the vehicle 1000 such that the vehicle 1000 followsthe trajectory that is determined by the trajectory controller. Forexample, the output of the trajectory controller can be an optimizedtrajectory that may be supplied to the powertrain 1200, the wheels 1400,or both. In some embodiments, the optimized trajectory can be controlinputs such as a set of steering angles, with each steering anglecorresponding to a point in time or a position. In some embodiments, theoptimized trajectory can be one or more paths, lines, curves, or acombination thereof.

One or more of the wheels 1400 may be a steered wheel, which may bepivoted to a steering angle under control of the steering unit 1230, apropelled wheel, which may be torqued to propel the vehicle 1000 undercontrol of the transmission 1220, or a steered and propelled wheel thatmay steer and propel the vehicle 1000.

Although not shown in FIG. 1, a vehicle may include units, or elementsnot shown in FIG. 1, such as an enclosure, a Bluetooth® module, afrequency modulated (FM) radio unit, a Near Field Communication (NFC)module, a liquid crystal display (LCD) display unit, an organiclight-emitting diode (OLED) display unit, a speaker, or any combinationthereof.

In some embodiments, the vehicle 1000 may be an autonomous vehicle. Anautonomous vehicle may be controlled autonomously, without direct humanintervention, to traverse a portion of a vehicle transportation network.Although not shown separately in FIG. 1, in some implementations, anautonomous vehicle may include an autonomous vehicle control unit, whichmay perform autonomous vehicle routing, navigation, and control. In someimplementations, the autonomous vehicle control unit may be integratedwith another unit of the vehicle. For example, the controller 1300 mayinclude the autonomous vehicle control unit.

In some implementations, the autonomous vehicle control unit may controlor operate the vehicle 1000 to traverse a portion of the vehicletransportation network in accordance with current vehicle operationparameters. In another example, the autonomous vehicle control unit maycontrol or operate the vehicle 1000 to perform a defined operation ormaneuver, such as parking the vehicle. In another example, autonomousvehicle control unit may generate a route of travel from an origin, suchas a current location of the vehicle 1000, to a destination based onvehicle information, environment information, vehicle transportationnetwork information representing the vehicle transportation network, ora combination thereof, and may control or operate the vehicle 1000 totraverse the vehicle transportation network in accordance with theroute. For example, the autonomous vehicle control unit may output theroute of travel to a trajectory controller that may operate the vehicle1000 to travel from the origin to the destination using the generatedroute.

FIG. 2 is a diagram of an example of a portion of a vehicletransportation and communication system in which the aspects, features,and elements disclosed herein may be implemented. The vehicletransportation and communication system 2000 may include one or morevehicles 2100/2110, such as the vehicle 1000 shown in FIG. 1, which maytravel via one or more portions of one or more vehicle transportationnetworks 2200, and may communicate via one or more electroniccommunication networks 2300. Although not explicitly shown in FIG. 2, avehicle may traverse an area that is not expressly or completelyincluded in a vehicle transportation network, such as an off-road area.

In some embodiments, the electronic communication network 2300 may be,for example, a multiple access system and may provide for communication,such as voice communication, data communication, video communication,messaging communication, or a combination thereof, between the vehicle2100/2110 and one or more communication devices 2400. For example, avehicle 2100/2110 may receive information, such as informationrepresenting the vehicle transportation network 2200, from acommunication device 2400 via the network 2300.

In some embodiments, a vehicle 2100/2110 may communicate via a wiredcommunication link (not shown), a wireless communication link2310/2320/2370, or a combination of any number of wired or wirelesscommunication links. For example, as shown, a vehicle 2100/2110 maycommunicate via a terrestrial wireless communication link 2310, via anon-terrestrial wireless communication link 2320, or via a combinationthereof. In some implementations, a terrestrial wireless communicationlink 2310 may include an Ethernet link, a serial link, a Bluetooth link,an infrared (IR) link, an ultraviolet (UV) link, or any link capable ofproviding for electronic communication.

In some embodiments, a vehicle 2100/2110 may communicate with anothervehicle 2100/2110. For example, a host, or subject, vehicle (HV) 2100may receive one or more automated inter-vehicle messages, such as abasic safety message (BSM), from a remote, or target, vehicle (RV) 2110,via a direct communication link 2370, or via a network 2300. Forexample, the remote vehicle 2110 may broadcast the message to hostvehicles within a defined broadcast range, such as 300 meters. In someembodiments, the host vehicle 2100 may receive a message via a thirdparty, such as a signal repeater (not shown) or another remote vehicle(not shown). In some embodiments, a vehicle 2100/2110 may transmit oneor more automated inter-vehicle messages periodically, based on, forexample, a defined interval, such as 100 milliseconds.

Automated inter-vehicle messages may include vehicle identificationinformation, geospatial state information, such as longitude, latitude,or elevation information, geospatial location accuracy information,kinematic state information, such as vehicle acceleration information,yaw rate information, speed information, vehicle heading information,braking system status information, throttle information, steering wheelangle information, or vehicle routing information, or vehicle operatingstate information, such as vehicle size information, headlight stateinformation, turn signal information, wiper status information,transmission information, or any other information, or combination ofinformation, relevant to the transmitting vehicle state. For example,transmission state information may indicate whether the transmission ofthe transmitting vehicle is in a neutral state, a parked state, aforward state, or a reverse state.

In some embodiments, the vehicle 2100 may communicate with thecommunications network 2300 via an access point 2330. An access point2330, which may include a computing device, may be configured tocommunicate with a vehicle 2100, with a communication network 2300, withone or more communication devices 2400, or with a combination thereofvia wired or wireless communication links 2310/2340. For example, anaccess point 2330 may be a base station, a base transceiver station(BTS), a Node-B, an enhanced Node-B (eNode-B), a Home Node-B (HNode-B),a wireless router, a wired router, a hub, a relay, a switch, or anysimilar wired or wireless device. Although shown as a single unit, anaccess point may include any number of interconnected elements.

In some embodiments, the vehicle 2100 may communicate with thecommunications network 2300 via a satellite 2350, or othernon-terrestrial communication device. A satellite 2350, which mayinclude a computing device, may be configured to communicate with avehicle 2100, with a communication network 2300, with one or morecommunication devices 2400, or with a combination thereof via one ormore communication links 2320/2360. Although shown as a single unit, asatellite may include any number of interconnected elements.

An electronic communication network 2300 may be any type of networkconfigured to provide for voice, data, or any other type of electroniccommunication. For example, the electronic communication network 2300may include a local area network (LAN), a wide area network (WAN), avirtual private network (VPN), a mobile or cellular telephone network,the Internet, or any other electronic communication system. Theelectronic communication network 2300 may use a communication protocol,such as the transmission control protocol (TCP), the user datagramprotocol (UDP), the internet protocol (IP), the real-time transportprotocol (RTP) the HyperText Transport Protocol (HTTP), or a combinationthereof. Although shown as a single unit, an electronic communicationnetwork may include any number of interconnected elements.

In some embodiments, a vehicle 2100 may identify a portion or conditionof the vehicle transportation network 2200. For example, the vehicle mayinclude one or more on-vehicle sensors 2105, such as sensor 1360 shownin FIG. 1, which may include a speed sensor, a wheel speed sensor, acamera, a gyroscope, an optical sensor, a laser sensor, a radar sensor,a sonic sensor, or any other sensor or device or combination thereofcapable of determining or identifying a portion or condition of thevehicle transportation network 2200.

In some embodiments, a vehicle 2100 may traverse a portion or portionsof one or more vehicle transportation networks 2200 using informationcommunicated via the network 2300, such as information representing thevehicle transportation network 2200, information identified by one ormore on-vehicle sensors 2105, or a combination thereof.

Although, for simplicity, FIG. 2 shows one vehicle 2100, one vehicletransportation network 2200, one electronic communication network 2300,and one communication device 2400, any number of vehicles, networks, orcomputing devices may be used. In some embodiments, the vehicletransportation and communication system 2000 may include devices, units,or elements not shown in FIG. 2. Although the vehicle 2100 is shown as asingle unit, a vehicle may include any number of interconnectedelements.

Although the vehicle 2100 is shown communicating with the communicationdevice 2400 via the network 2300, the vehicle 2100 may communicate withthe communication device 2400 via any number of direct or indirectcommunication links. For example, the vehicle 2100 may communicate withthe communication device 2400 via a direct communication link, such as aBluetooth communication link.

In some embodiments, a vehicle 2100/2210 may be associated with anentity 2500/2510, such as a driver, operator, or owner of the vehicle.In some embodiments, an entity 2500/2510 associated with a vehicle2100/2110 may be associated with one or more personal electronic devices2502/2504/2512/2514, such as a smartphone 2502/2512 or a computer2504/2514. In some embodiments, a personal electronic device2502/2504/2512/2514 may communicate with a corresponding vehicle2100/2110 via a direct or indirect communication link. Although oneentity 2500/2510 is shown as associated with one vehicle 2100/2110 inFIG. 2, any number of vehicles may be associated with an entity and anynumber of entities may be associated with a vehicle.

FIG. 3 is a diagram of a portion of a vehicle transportation network inaccordance with this disclosure. A vehicle transportation network 3000may include one or more unnavigable areas 3100, such as a building, oneor more partially navigable areas, such as parking area 3200, one ormore navigable areas, such as roads 3300/3400, or a combination thereof.In some embodiments, an autonomous vehicle, such as the vehicle 1000shown in FIG. 1, one of the vehicles 2100/2110 shown in FIG. 2, asemi-autonomous vehicle, or any other vehicle implementing autonomousdriving, may traverse a portion or portions of the vehicletransportation network 3000.

The vehicle transportation network may include one or more interchanges3210 between one or more navigable, or partially navigable, areas3200/3300/3400. For example, the portion of the vehicle transportationnetwork shown in FIG. 3 includes an interchange 3210 between the parkingarea 3200 and road 3400. In some embodiments, the parking area 3200 mayinclude parking slots 3220.

A portion of the vehicle transportation network, such as a road3300/3400, may include one or more lanes 3320/3340/3360/3420/3440 andmay be associated with one or more directions of travel, which areindicated by arrows in FIG. 3.

In some embodiments, a vehicle transportation network, or a portionthereof, such as the portion of the vehicle transportation network shownin FIG. 3, may be represented as vehicle transportation networkinformation. For example, vehicle transportation network information maybe expressed as a hierarchy of elements, such as markup languageelements, which may be stored in a database or file. For simplicity, thefigures herein depict vehicle transportation network informationrepresenting portions of a vehicle transportation network as diagrams ormaps; however, vehicle transportation network information may beexpressed in any computer-usable form capable of representing a vehicletransportation network, or a portion thereof. In some embodiments, thevehicle transportation network information may include vehicletransportation network control information, such as direction of travelinformation, speed limit information, toll information, gradeinformation, such as inclination or angle information, surface materialinformation, aesthetic information or a combination thereof.

In some embodiments, a portion, or a combination of portions, of thevehicle transportation network may be identified as a point of interestor a destination. For example, the vehicle transportation networkinformation may identify a building, such as the unnavigable area 3100,and the adjacent partially navigable parking area 3200 as a point ofinterest, an autonomous vehicle may identify the point of interest as adestination, and the autonomous vehicle may travel from an origin to thedestination by traversing the vehicle transportation network. Althoughthe parking area 3200 associated with the unnavigable area 3100 is shownas adjacent to the unnavigable area 3100 in FIG. 3, a destination mayinclude, for example, a building and a parking area that is physicallyor geospatially non-adjacent to the building.

In some embodiments, identifying a destination may include identifying alocation for the destination, which may be a discrete uniquelyidentifiable geolocation. For example, the vehicle transportationnetwork may include a defined location, such as a street address, apostal address, a vehicle transportation network address, a GPS address,or a combination thereof for the destination.

In some embodiments, a destination may be associated with one or moreentrances, such as the entrance 3500 shown in FIG. 3. In someembodiments, the vehicle transportation network information may includedefined entrance location information, such as information identifying ageolocation of an entrance associated with a destination. In someembodiments, predicted entrance location information may be determinedas described herein.

In some embodiments, the vehicle transportation network may beassociated with, or may include, a pedestrian transportation network.For example, FIG. 3 includes a portion 3600 of a pedestriantransportation network, which may be a pedestrian walkway. In someembodiments, a pedestrian transportation network, or a portion thereof,such as the portion 3600 of the pedestrian transportation network shownin FIG. 3, may be represented as pedestrian transportation networkinformation. In some embodiments, the vehicle transportation networkinformation may include pedestrian transportation network information. Apedestrian transportation network may include pedestrian navigableareas. A pedestrian navigable area, such as a pedestrian walkway or asidewalk, may correspond with a non-navigable area of a vehicletransportation network. Although not shown separately in FIG. 3, apedestrian navigable area, such as a pedestrian crosswalk, maycorrespond with a navigable area, or a partially navigable area, of avehicle transportation network.

In some embodiments, a destination may be associated with one or moredocking locations, such as the docking location 3700 shown in FIG. 3. Adocking location 3700 may be a designated or undesignated location orarea in proximity to a destination at which an autonomous vehicle maystop, stand, or park such that docking operations, such as passengerloading or unloading, may be performed.

In some embodiments, the vehicle transportation network information mayinclude docking location information, such as information identifying ageolocation of one or more docking locations 3700 associated with adestination. In some embodiments, the docking location information maybe defined docking location information, which may be docking locationinformation manually included in the vehicle transportation networkinformation. For example, defined docking location information may beincluded in the vehicle transportation network information based on userinput. In some embodiments, the docking location information may beautomatically generated docking location information as describedherein. Although not shown separately in FIG. 3, docking locationinformation may identify a type of docking operation associated with adocking location 3700. For example, a destination may be associated witha first docking location for passenger loading and a second dockinglocation for passenger unloading. Although an autonomous vehicle maypark at a docking location, a docking location associated with adestination may be independent and distinct from a parking areaassociated with the destination.

In an example, an autonomous vehicle may identify a point of interest,which may include the unnavigable area 3100, the parking area 3200, andthe entrance 3500, as a destination. The autonomous vehicle may identifythe unnavigable area 3100, or the entrance 3500, as a primarydestination for the point of interest, and may identify the parking area3200 as a secondary destination. The autonomous vehicle may identify thedocking location 3700 as a docking location for the primary destination.The autonomous vehicle may generate a route from an origin (not shown)to the docking location 3700. The autonomous vehicle may traverse thevehicle transportation network from the origin to the docking location3700 using the route. The autonomous vehicle may stop or park at thedocking location 3700 such that passenger loading or unloading may beperformed. The autonomous vehicle may generate a subsequent route fromthe docking location 3700 to the parking area 3200, may traverse thevehicle transportation network from the docking location 3700 to theparking area 3200 using the subsequent route, and may park in theparking area 3200.

FIG. 4 is a diagram of an example of an autonomous vehicle operationalmanagement system 4000 in accordance with embodiments of thisdisclosure. The autonomous vehicle operational management system 4000may be implemented in an autonomous vehicle, such as the vehicle 1000shown in FIG. 1, one of the vehicles 2100/2110 shown in FIG. 2, asemi-autonomous vehicle, or any other vehicle implementing autonomousdriving.

An autonomous vehicle may traverse a vehicle transportation network, ora portion thereof, which may include traversing distinct vehicleoperational scenarios. A distinct vehicle operational scenario mayinclude any distinctly identifiable set of operative conditions that mayaffect the operation of the autonomous vehicle within a definedspatiotemporal area, or operational environment, of the autonomousvehicle. For example, a distinct vehicle operational scenario may bebased on a number or cardinality of roads, road segments, or lanes thatthe autonomous vehicle may traverse within a defined spatiotemporaldistance. In another example, a distinct vehicle operational scenariomay be based on one or more traffic control devices that may affect theoperation of the autonomous vehicle within a defined spatiotemporalarea, or operational environment, of the autonomous vehicle. In anotherexample, a distinct vehicle operational scenario may be based on one ormore identifiable rules, regulations, or laws that may affect theoperation of the autonomous vehicle within a defined spatiotemporalarea, or operational environment, of the autonomous vehicle. In anotherexample, a distinct vehicle operational scenario may be based on one ormore identifiable external objects that may affect the operation of theautonomous vehicle within a defined spatiotemporal area, or operationalenvironment, of the autonomous vehicle.

Examples of distinct vehicle operational scenarios including a distinctvehicle operational scenario wherein the autonomous vehicle istraversing an intersection; a distinct vehicle operational scenariowherein a pedestrian is crossing, or approaching, the expected path ofthe autonomous vehicle; and a distinct vehicle operational scenariowherein the autonomous vehicle is changing lanes.

For simplicity and clarity, similar vehicle operational scenarios may bedescribed herein with reference to vehicle operational scenario types orclasses. For example, vehicle operational scenarios includingpedestrians may be referred to herein as pedestrian scenarios referringto the types or classes of vehicle operational scenarios that includepedestrians. As an example, a first pedestrian vehicle operationalscenario may include a pedestrian crossing a road at a crosswalk and assecond pedestrian vehicle operational scenario may include a pedestriancrossing a road by jaywalking. Although pedestrian vehicle operationalscenarios, intersection vehicle operational scenarios, and lane changevehicle operational scenarios are described herein, any other vehicleoperational scenario or vehicle operational scenario type may be used.

Aspects of the operational environment of the autonomous vehicle may berepresented within respective distinct vehicle operational scenarios.For example, the relative orientation, trajectory, expected path, ofexternal objects may be represented within respective distinct vehicleoperational scenarios. In another example, the relative geometry of thevehicle transportation network may be represented within respectivedistinct vehicle operational scenarios.

As an example, a first distinct vehicle operational scenario maycorrespond to a pedestrian crossing a road at a crosswalk, and arelative orientation and expected path of the pedestrian, such ascrossing from left to right for crossing from right to left, may berepresented within the first distinct vehicle operational scenario. Asecond distinct vehicle operational scenario may correspond to apedestrian crossing a road by jaywalking, and a relative orientation andexpected path of the pedestrian, such as crossing from left to right forcrossing from right to left, may be represented within the seconddistinct vehicle operational scenario.

In some embodiments, an autonomous vehicle may traverse multipledistinct vehicle operational scenarios within an operationalenvironment, which may be aspects of a compound vehicle operationalscenario. For example, a pedestrian may approach the expected path forthe autonomous vehicle traversing an intersection.

The autonomous vehicle operational management system 4000 may operate orcontrol the autonomous vehicle to traverse the distinct vehicleoperational scenarios subject to defined constraints, such as safetyconstraints, legal constraints, physical constraints, user acceptabilityconstraints, or any other constraint or combination of constraints thatmay be defined or derived for the operation of the autonomous vehicle.

In some embodiments, controlling the autonomous vehicle to traverse thedistinct vehicle operational scenarios may include identifying ordetecting the distinct vehicle operational scenarios, identifyingcandidate vehicle control actions based on the distinct vehicleoperational scenarios, controlling the autonomous vehicle to traverse aportion of the vehicle transportation network in accordance with one ormore of the candidate vehicle control actions, or a combination thereof.

A vehicle control action may indicate a vehicle control operation ormaneuver, such as accelerating, decelerating, turning, stopping, or anyother vehicle operation or combination of vehicle operations that may beperformed by the autonomous vehicle in conjunction with traversing aportion of the vehicle transportation network.

The autonomous vehicle operational management controller 4100, oranother unit of the autonomous vehicle, may control the autonomousvehicle to traverse the vehicle transportation network, or a portionthereof, in accordance with a vehicle control action.

For example, the autonomous vehicle operational management controller4100 may control the autonomous vehicle to traverse the vehicletransportation network, or a portion thereof, in accordance with a‘stop’ vehicle control action by stopping the autonomous vehicle orotherwise controlling the autonomous vehicle to become or remainstationary.

In another example, the autonomous vehicle operational managementcontroller 4100 may control the autonomous vehicle to traverse thevehicle transportation network, or a portion thereof, in accordance withan ‘advance’ vehicle control action by slowly inching forward a shortdistance, such as a few inches or a foot.

In another example, the autonomous vehicle operational managementcontroller 4100 may control the autonomous vehicle to traverse thevehicle transportation network, or a portion thereof, in accordance withan ‘accelerate’ vehicle control action by accelerating a definedacceleration rate, or at an acceleration rate within a defined range.

In another example, the autonomous vehicle operational managementcontroller 4100 may control the autonomous vehicle to traverse thevehicle transportation network, or a portion thereof, in accordance witha ‘decelerate’ vehicle control action by decelerating a defineddeceleration rate, or at a deceleration rate within a defined range.

In another example, the autonomous vehicle operational managementcontroller 4100 may control the autonomous vehicle to traverse thevehicle transportation network, or a portion thereof, in accordance witha ‘maintain’ vehicle control action by controlling the autonomousvehicle to traverse the vehicle transportation network, or a portionthereof, in accordance with current operational parameters, such as bymaintaining a current velocity, maintaining a current path or route,maintaining a current lane orientation, or the like.

In another example, the autonomous vehicle operational managementcontroller 4100 may control the autonomous vehicle to traverse thevehicle transportation network, or a portion thereof, in accordance witha ‘proceed’ vehicle control action by controlling the autonomous vehicleto traverse the vehicle transportation network, or a portion thereof, bybeginning or resuming a previously identified set of operationalparameters, which may include controlling the autonomous vehicle totraverse the vehicle transportation network, or a portion thereof, inaccordance with one or more other vehicle control actions. For example,the autonomous vehicle may be stationary at an intersection, anidentified route for the autonomous vehicle may include traversingthrough the intersection, and controlling the autonomous vehicle inaccordance with a ‘proceed’ vehicle control action may includecontrolling the autonomous vehicle to accelerate at a definedacceleration rate to a defined velocity along the identified path. Inanother example, the autonomous vehicle may be traversing a portion ofthe vehicle transportation network at a defined velocity, a lane changemay be identified for the autonomous vehicle, and controlling theautonomous vehicle in accordance with a ‘proceed’ vehicle control actionmay include controlling the autonomous vehicle to perform a sequence oftrajectory adjustments in accordance with defined lane change parameterssuch that the autonomous vehicle performs the identified lane changeoperation.

In some embodiments, a vehicle control action may include one or moreperformance metrics. For example, a ‘stop’ vehicle control action mayinclude a deceleration rate as a performance metric. In another example,a ‘proceed’ vehicle control action may expressly indicate route or pathinformation, speed information, an acceleration rate, or a combinationthereof as performance metrics, or may expressly or implicitly indicatethat a current or previously identified path, speed, acceleration rate,or a combination thereof may be maintained.

In some embodiments, a vehicle control action may be a compound vehiclecontrol action, which may include a sequence, combination, or both ofvehicle control actions. For example, an ‘advance’ vehicle controlaction may indicate a ‘stop’ vehicle control action, a subsequent‘accelerate’ vehicle control action associated with a definedacceleration rate, and a subsequent ‘stop’ vehicle control actionassociated with a defined deceleration rate, such that controlling theautonomous vehicle in accordance with the ‘advance’ vehicle controlaction includes controlling the autonomous vehicle to slowly inchforward a short distance, such as a few inches or a foot.

In some embodiments, the autonomous vehicle operational managementsystem 4000 may include an autonomous vehicle operational managementcontroller 4100, a blocking monitor 4200, operational environmentmonitors 4300, scenario-specific operation control evaluation modules4400, or a combination thereof. Although described separately, theblocking monitor 4200 may be an instance, or instances, of anoperational environment monitor 4300.

The autonomous vehicle operational management controller 4100 mayreceive, identify, or otherwise access, operational environmentinformation representing an operational environment for the autonomousvehicle, such as a current operational environment or an expectedoperational environment, or one or more aspects thereof. The operationalenvironment of the autonomous vehicle may include a distinctlyidentifiable set of operative conditions that may affect the operationof the autonomous vehicle within a defined spatiotemporal area of theautonomous vehicle.

For example, the operational environment information may include vehicleinformation for the autonomous vehicle, such as information indicating ageospatial location of the autonomous vehicle, information correlatingthe geospatial location of the autonomous vehicle to informationrepresenting the vehicle transportation network, a route of theautonomous vehicle, a speed of the autonomous vehicle, an accelerationstate of the autonomous vehicle, passenger information of the autonomousvehicle, or any other information about the autonomous vehicle or theoperation of the autonomous vehicle.

In another example, the operational environment information may includeinformation representing the vehicle transportation network proximate tothe autonomous vehicle, such as within a defined spatial distance of theautonomous vehicle, such as 300 meters, information indicating thegeometry of one or more aspects of the vehicle transportation network,information indicating a condition, such as a surface condition, of thevehicle transportation network, or any combination thereof.

In another example, the operational environment information may includeinformation representing external objects within the operationalenvironment of the autonomous vehicle, such as information representingpedestrians, non-human animals, non-motorized transportation devices,such as bicycles or skateboards, motorized transportation devices, suchas remote vehicles, or any other external object or entity that mayaffect the operation of the autonomous vehicle.

In some embodiments, the autonomous vehicle operational managementcontroller 4100 may monitor the operational environment of theautonomous vehicle, or defined aspects thereof. In some embodiments,monitoring the operational environment of the autonomous vehicle mayinclude identifying and tracking external objects, identifying distinctvehicle operational scenarios, or a combination thereof.

For example, the autonomous vehicle operational management controller4100 may identify and track external objects with the operationalenvironment of the autonomous vehicle. Identifying and tracking theexternal objects may include identifying spatiotemporal locations ofrespective external objects, which may be relative to the autonomousvehicle, identifying one or more expected paths for respective externalobjects, which may include identifying a speed, a trajectory, or both,for an external object. For simplicity and clarity, descriptions oflocations, expected locations, paths, expected paths, and the likeherein may omit express indications that the corresponding locations andpaths refer to geospatial and temporal components; however, unlessexpressly indicated herein, or otherwise unambiguously clear fromcontext, the locations, expected locations, paths, expected paths, andthe like described herein may include geospatial components, temporalcomponents, or both.

In some embodiments, the operational environment monitors 4300 mayinclude an operational environment monitor 4310 for monitoringpedestrians (pedestrian monitor), an operational environment monitor4320 for monitoring intersections (intersection monitor), an operationalenvironment monitor 4330 for monitoring lane changes (lane changemonitor), or a combination thereof. An operational environment monitor4340 is shown using broken lines to indicate that the autonomous vehicleoperational management system 4000 may include any number of operationalenvironment monitors 4300.

One or more distinct vehicle operational scenarios may be monitored by arespective operational environment monitor 4300. For example, thepedestrian monitor 4310 may monitor operational environment informationcorresponding to multiple pedestrian vehicle operational scenarios, theintersection monitor 4320 may monitor operational environmentinformation corresponding to multiple intersection vehicle operationalscenarios, and the lane change monitor 4330 may monitor operationalenvironment information corresponding to multiple lane change vehicleoperational scenarios.

An operational environment monitor 4300 may receive, or otherwiseaccess, operational environment information, such as operationalenvironment information generated or captured by one or more sensors ofthe autonomous vehicle, vehicle transportation network information,vehicle transportation network geometry information, or a combinationthereof. For example, the operational environment monitor 4310 formonitoring pedestrians may receive, or otherwise access, information,such as sensor information, which may indicate, correspond to, or mayotherwise be associated with, one or more pedestrians in the operationalenvironment of the autonomous vehicle.

In some embodiments, an operational environment monitor 4300 mayassociate the operational environment information, or a portion thereof,with the operational environment, or an aspect thereof, such as with anexternal object, such as a pedestrian, a remote vehicle, or an aspect ofthe vehicle transportation network geometry.

In some embodiments, an operational environment monitor 4300 maygenerate, or otherwise identify, information representing one or moreaspects of the operational environment, such as with an external object,such as a pedestrian, a remote vehicle, or an aspect of the vehicletransportation network geometry, which may include filtering,abstracting, or otherwise processing the operational environmentinformation.

In some embodiments, an operational environment monitor 4300 may outputthe information representing the one or more aspects of the operationalenvironment to, or for access by, the autonomous vehicle operationalmanagement controller 4100, such by storing the information representingthe one or more aspects of the operational environment in a memory, suchas the memory 1340 shown in FIG. 1, of the autonomous vehicle accessibleby the autonomous vehicle operational management controller 4100,sending the information representing the one or more aspects of theoperational environment to the autonomous vehicle operational managementcontroller 4100, or a combination thereof. In some embodiments, anoperational environment monitor 4300 may output the informationrepresenting the one or more aspects of the operational environment toone or more elements of the autonomous vehicle operational managementsystem 4000, such as the blocking monitor 4200.

For example, the operational environment monitor 4310 for monitoringpedestrians may correlate, associate, or otherwise process theoperational environment information to identify, track, or predictactions of one or more pedestrians. For example, the operationalenvironment monitor 4310 for monitoring pedestrians may receiveinformation, such as sensor information, from one or more sensors, whichmay correspond to one or more pedestrians, the operational environmentmonitor 4310 for monitoring pedestrians may associate the sensorinformation with one or more identified pedestrians, which may includemay identifying a direction of travel, a path, such as an expected path,a current or expected velocity, a current or expected acceleration rate,or a combination thereof for one or more of the respective identifiedpedestrians, and the operational environment monitor 4310 for monitoringpedestrians may output the identified, associated, or generatedpedestrian information to, or for access by, the autonomous vehicleoperational management controller 4100.

In another example, the operational environment monitor 4320 formonitoring intersections may correlate, associate, or otherwise processthe operational environment information to identify, track, or predictactions of one or more remote vehicles in the operational environment ofthe autonomous vehicle, to identify an intersection, or an aspectthereof, in the operational environment of the autonomous vehicle, toidentify vehicle transportation network geometry, or a combinationthereof. For example, the operational environment monitor 4310 formonitoring intersections may receive information, such as sensorinformation, from one or more sensors, which may correspond to one ormore remote vehicles in the operational environment of the autonomousvehicle, the intersection, or one or more aspects thereof, in theoperational environment of the autonomous vehicle, the vehicletransportation network geometry, or a combination thereof, theoperational environment monitor 4310 for monitoring intersections mayassociate the sensor information with one or more identified remotevehicles in the operational environment of the autonomous vehicle, theintersection, or one or more aspects thereof, in the operationalenvironment of the autonomous vehicle, the vehicle transportationnetwork geometry, or a combination thereof, which may include mayidentifying a current or expected direction of travel, a path, such asan expected path, a current or expected velocity, a current or expectedacceleration rate, or a combination thereof for one or more of therespective identified remote vehicles, and the operational environmentmonitor 4320 for monitoring intersections may output the identified,associated, or generated intersection information to, or for access by,the autonomous vehicle operational management controller 4100.

In another example, operational environment monitor 4330 for monitoringlane changing may correlate, associate, or otherwise process theoperational environment information to identify, track, or predictactions of one or more remote vehicles in the operational environment ofthe autonomous vehicle, such as information indicating a slow orstationary remote vehicle along the expected path of the autonomousvehicle, to identify one or more aspects of the operational environmentof the autonomous vehicle, such as vehicle transportation networkgeometry in the operational environment of the autonomous vehicle, or acombination thereof geospatially corresponding to a current or expectedlane change operation. For example, the operational environment monitor4330 for monitoring lane changing may receive information, such assensor information, from one or more sensors, which may correspond toone or more remote vehicles in the operational environment of theautonomous vehicle, one or more aspects of the operational environmentof the autonomous vehicle in the operational environment of theautonomous vehicle or a combination thereof geospatially correspondingto a current or expected lane change operation, the operationalenvironment monitor 4330 for monitoring lane changing may associate thesensor information with one or more identified remote vehicles in theoperational environment of the autonomous vehicle, one or more aspectsof the operational environment of the autonomous vehicle or acombination thereof geospatially corresponding to a current or expectedlane change operation, which may include may identifying a current orexpected direction of travel, a path, such as an expected path, acurrent or expected velocity, a current or expected acceleration rate,or a combination thereof for one or more of the respective identifiedremote vehicles, and the operational environment monitor 4330 formonitoring intersections may output the identified, associated, orgenerated lane change information to, or for access by, the autonomousvehicle operational management controller 4100.

The autonomous vehicle operational management controller 4100 mayidentify one or more distinct vehicle operational scenarios based on oneor more aspects of the operational environment represented by theoperational environment information. For example, the autonomous vehicleoperational management controller 4100 may identify a distinct vehicleoperational scenario in response to identifying, or based on, theoperational environment information indicated by one or more of theoperational environment monitors 4300.

In some embodiments, the autonomous vehicle operational managementcontroller 4100 may identify multiple distinct vehicle operationalscenarios based on one or more aspects of the operational environmentrepresented by the operational environment information. For example, theoperational environment information may include information representinga pedestrian approaching an intersection along an expected path for theautonomous vehicle, and the autonomous vehicle operational managementcontroller 4100 may identify a pedestrian vehicle operational scenario,an intersection vehicle operational scenario, or both.

The autonomous vehicle operational management controller 4100 mayinstantiate respective instances of one or more of the scenario-specificoperational control evaluation modules 4400 based on one or more aspectsof the operational environment represented by the operationalenvironment information. For example, the autonomous vehicle operationalmanagement controller 4100 may instantiate the instance of thescenario-specific operational control evaluation module 4400 in responseto identifying the distinct vehicle operational scenario.

In some embodiments, the autonomous vehicle operational managementcontroller 4100 may instantiate multiple instances of one or morescenario-specific operational control evaluation modules 4400 based onone or more aspects of the operational environment represented by theoperational environment information. For example, the operationalenvironment information may indicate two pedestrians in the operationalenvironment of the autonomous vehicle and the autonomous vehicleoperational management controller 4100 may instantiate a respectiveinstance of the pedestrian-scenario-specific operational controlevaluation module 4410 for each pedestrian based on one or more aspectsof the operational environment represented by the operationalenvironment information.

In some embodiments, the cardinality, number, or count, of identifiedexternal objects, such as pedestrians or remote vehicles, correspondingto a scenario, such as the pedestrian scenario, the intersectionscenario, or the lane change scenario, may exceed a defined threshold,which may be a defined scenario-specific threshold, and the autonomousvehicle operational management controller 4100 may omit instantiating aninstance of a scenario-specific operational control evaluation module4400 corresponding to one or more of the identified external objects.

For example, the operational environment information indicated by theoperational environment monitors 4300 may indicate twenty-fivepedestrians in the operational environment of the autonomous vehicle,the defined threshold for the pedestrian scenario may be a definedcardinality, such as ten, of pedestrians, the autonomous vehicleoperational management controller 4100 may identify the ten mostrelevant pedestrians, such as the ten pedestrians geospatially mostproximate to the autonomous vehicle having converging expected pathswith the autonomous vehicle, the autonomous vehicle operationalmanagement controller 4100 may instantiate ten instances of thepedestrian-scenario-specific operational control evaluation module 4410for the ten most relevant pedestrians, and the autonomous vehicleoperational management controller 4100 may omit instantiating instancesof the pedestrian-scenario-specific operational control evaluationmodule 4410 for the fifteen other pedestrians.

In another example, the operational environment information indicated bythe operational environment monitors 4300 may indicate an intersectionincluding four road segments, such as a northbound road segment, asouthbound road segment, an eastbound road segment, and a westbound roadsegment, and indicating five remote vehicles corresponding to thenorthbound road segment, three remote vehicles corresponding to thesouthbound road segment, four remote vehicles corresponding to theeastbound road segment, and two remote vehicles corresponding to thewestbound road segment, the defined threshold for the intersectionscenario may be a defined cardinality, such as two, of remote vehiclesper road segment, the autonomous vehicle operational managementcontroller 4100 may identify the two most relevant remote vehicles perroad segment, such as the two remote vehicles geospatially mostproximate to the intersection having converging expected paths with theautonomous vehicle per road segment, the autonomous vehicle operationalmanagement controller 4100 may instantiate two instances of theintersection-scenario-specific operational control evaluation module4420 for the two most relevant remote vehicles corresponding to thenorthbound road segment, two instances of theintersection-scenario-specific operational control evaluation module4420 for the two most relevant remote vehicles corresponding to thesouthbound road segment, two instances of theintersection-scenario-specific operational control evaluation module4420 for the two most relevant remote vehicles corresponding to theeastbound road segment, and two instances of theintersection-scenario-specific operational control evaluation module4420 for the two remote vehicles corresponding to the westbound roadsegment, and the autonomous vehicle operational management controller4100 may omit instantiating instances of theintersection-scenario-specific operational control evaluation module4420 for the three other remote vehicles corresponding to the northboundroad segment, the other remote vehicle corresponding to the southboundroad segment, and the two other remote vehicles corresponding to theeastbound road segment. Alternatively, or in addition, the definedthreshold for the intersection scenario may be a defined cardinality,such as eight, remote vehicles per intersection, and the autonomousvehicle operational management controller 4100 may identify the eightmost relevant remote vehicles for the intersection, such as the eightremote vehicles geospatially most proximate to the intersection havingconverging expected paths with the autonomous vehicle, the autonomousvehicle operational management controller 4100 may instantiate eightinstances of the intersection-scenario-specific operational controlevaluation module 4420 for the eight most relevant remote vehicles, andthe autonomous vehicle operational management controller 4100 may omitinstantiating instances of the intersection-scenario-specificoperational control evaluation module 4420 for the six other remotevehicles.

In some embodiments, the autonomous vehicle operational managementcontroller 4100 may send the operational environment information, or oneor more aspects thereof, to another unit of the autonomous vehicle, suchas the blocking monitor 4200 or one or more instances of thescenario-specific operational control evaluation modules 4400.

In some embodiments, the autonomous vehicle operational managementcontroller 4100 may store the operational environment information, orone or more aspects thereof, such as in a memory, such as the memory1340 shown in FIG. 1, of the autonomous vehicle.

The autonomous vehicle operational management controller 4100 mayreceive candidate vehicle control actions from respective instances ofthe scenario-specific operational control evaluation modules 4400. Forexample, a candidate vehicle control action from a first instance of afirst scenario-specific operational control evaluation module 4400 mayindicate a ‘stop’ vehicle control action, a candidate vehicle controlaction from a second instance of a second scenario-specific operationalcontrol evaluation module 4400 may indicate an ‘advance’ vehicle controlaction, and a candidate vehicle control action from a third instance ofa third scenario-specific operational control evaluation module 4400 mayindicate a ‘proceed’ vehicle control action.

The autonomous vehicle operational management controller 4100 maydetermine whether to traverse a portion of the vehicle transportationnetwork in accordance with one or more candidate vehicle controlactions. For example, the autonomous vehicle operational managementcontroller 4100 may receive multiple candidate vehicle control actionsfrom multiple instances of scenario-specific operational controlevaluation modules 4400, may identify a vehicle control action from thecandidate vehicle control actions, and may traverse the vehicletransportation network in accordance with the vehicle control action.

In some embodiments, the autonomous vehicle operational managementcontroller 4100 may identify a vehicle control action from the candidatevehicle control actions based on one or more defined vehicle controlaction identification metrics.

In some embodiments, the defined vehicle control action identificationmetrics may include a priority, weight, or rank, associated with eachtype of vehicle control action, and identifying the vehicle controlaction from the candidate vehicle control actions may includeidentifying a highest priority vehicle control action from the candidatevehicle control actions. For example, the ‘stop’ vehicle control actionmay be associated with a high priority, the ‘advance’ vehicle controlaction may be associated with an intermediate priority, which may belower than the high priority, and the ‘proceed’ vehicle control actionmay be associated with a low priority, which may be lower than theintermediate priority. In an example, the candidate vehicle controlactions may include one or more ‘stop’ vehicle control actions, and the‘stop’ vehicle control action may be identified as the vehicle controlaction. In another example, the candidate vehicle control actions mayomit a ‘stop’ vehicle control action, may include one or more ‘advance’vehicle control actions, and the ‘advance’ vehicle control action may beidentified as the vehicle control action. In another example, thecandidate vehicle control actions may omit a ‘stop’ vehicle controlaction, may omit an ‘advance’ vehicle control action, may include one ormore ‘proceed’ vehicle control actions, and the ‘proceed’ vehiclecontrol action may be identified as the vehicle control action.

In some embodiments, identifying the vehicle control action from thecandidate vehicle control actions may include generating or calculatinga weighted average for each type of vehicle control action based on thedefined vehicle control action identification metrics, the instantiatedscenarios, weights associated with the instantiated scenarios, thecandidate vehicle control actions, weights associated with the candidatevehicle control actions, or a combination thereof.

For example, identifying the vehicle control action from the candidatevehicle control actions may include implementing a machine learningcomponent, such as supervised learning of a classification problem, andtraining the machine learning component using examples, such as 1000examples, of the corresponding vehicle operational scenario. In anotherexample, identifying the vehicle control action from the candidatevehicle control actions may include implementing a Markov DecisionProcess, or a Partially Observable Markov Decision Processes, which maydescribe how respective candidate vehicle control actions affectsubsequent candidate vehicle control actions affect, and may include areward function that outputs a positive or negative reward forrespective vehicle control actions.

The autonomous vehicle operational management controller 4100 mayuninstantiate an instance of a scenario-specific operational controlevaluation module 4400. For example, the autonomous vehicle operationalmanagement controller 4100 may identify a distinct set of operativeconditions as indicating a distinct vehicle operational scenario for theautonomous vehicle, instantiate an instance of a scenario-specificoperational control evaluation module 4400 for the distinct vehicleoperational scenario, monitor the operative conditions, subsequentlydetermine that one or more of the operative conditions has expired, orhas a probability of affecting the operation of the autonomous vehiclebelow a defined threshold, and the autonomous vehicle operationalmanagement controller 4100 may uninstantiate the instance of thescenario-specific operational control evaluation module 4400.

The blocking monitor 4200 may receive operational environmentinformation representing an operational environment, or an aspectthereof, for the autonomous vehicle. For example, the blocking monitor4200 may receive the operational environment information from theautonomous vehicle operational management controller 4100, from a sensorof the autonomous vehicle, from an external device, such as a remotevehicle or an infrastructure device, or a combination thereof. In someembodiments, the blocking monitor 4200 may read the operationalenvironment information, or a portion thereof, from a memory, such as amemory of the autonomous vehicle, such as the memory 1340 shown in FIG.1.

Although not expressly shown in FIG. 4, the autonomous vehicleoperational management system 4000 may include a predictor module thatmay generate and send prediction information to the blocking monitor4200, and the blocking monitor 4200 may output probability ofavailability information to one or more of the operational environmentmonitors 4300.

The blocking monitor 4200 may determine a respective probability ofavailability, or corresponding blocking probability, for one or moreportions of the vehicle transportation network, such as portions of thevehicle transportation network proximal to the autonomous vehicle, whichmay include portions of the vehicle transportation network correspondingto an expected path of the autonomous vehicle, such as an expected pathidentified based on a current route of the autonomous vehicle.

A probability of availability, or corresponding blocking probability,may indicate a probability or likelihood that the autonomous vehicle maytraverse a portion of, or spatial location within, the vehicletransportation network safely, such as unimpeded by an external object,such as a remote vehicle or a pedestrian. For example, a portion of thevehicle transportation network may include an obstruction, such as astationary object, and a probability of availability for the portion ofthe vehicle transportation network may be low, such as 0%, which may beexpressed as a high blocking probability, such as 100%, for the portionof the vehicle transportation network.

The blocking monitor 4200 may identify a respective probability ofavailability for each of multiple portions of the vehicle transportationnetwork within an operational environment, such as within 300 meters, ofthe autonomous vehicle.

In some embodiments, the blocking monitor 4200 may identify a portion ofthe vehicle transportation network and a corresponding probability ofavailability based on operating information for the autonomous vehicle,operating information for one or more external objects, vehicletransportation network information representing the vehicletransportation network, or a combination thereof. In some embodiments,the operating information for the autonomous vehicle may includeinformation indicating a geospatial location of the autonomous vehiclein the vehicle transportation network, which may be a current locationor an expected location, such as an expected location identified basedon an expected path for the autonomous vehicle. In some embodiments, theoperating information for the external objects may indicate a respectivegeospatial location of one or more external objects in, or proximate to,the vehicle transportation network, which may be a current location oran expected location, such as an expected location identified based onan expected path for the respective external object.

In some embodiments, a probability of availability may be indicated bythe blocking monitor 4200 corresponding to each external object in theoperational environment of the autonomous vehicle and a geospatial areamay be associated with multiple probabilities of availabilitycorresponding to multiple external objects. In some embodiments, anaggregate probability of availability may be indicated by the blockingmonitor 4200 corresponding to each type of external object in theoperational environment of the autonomous vehicle, such as a probabilityof availability for pedestrians and a probability of availability forremote vehicles, and a geospatial area may be associated with multipleprobabilities of availability corresponding to multiple external objecttypes. In some embodiments, the blocking monitor 4200 may indicate oneaggregate probability of availability for each geospatial location,which may include multiple temporal probabilities of availability for ageographical location.

In some embodiments, the blocking monitor 4200 may identify externalobjects, track external objects, project location information, pathinformation, or both for external objects, or a combination thereof. Forexample, the blocking monitor 4200 may identify an external object andmay identify an expected path for the external object, which mayindicate a sequence of expected spatial locations, expected temporallocations, and corresponding probabilities.

In some embodiments, the blocking monitor may identify the expected pathfor an external object based on operational environment information,such as information indicating a current location of the externalobject, information indicating a current trajectory for the externalobject, information indicating a type of classification of the externalobject, such as information classifying the external object as apedestrian or a remote vehicle, vehicle transportation networkinformation, such as information indicating that the vehicletransportation network includes a crosswalk proximate to the externalobject, previously identified or tracked information associated with theexternal object, or any combination thereof. For example, the externalobject may be identified as a remote vehicle, and the expected path forthe remote vehicle may be identified based on information indicating acurrent location of the remote vehicle, information indicating a currenttrajectory of the remote vehicle, information indicating a current speedof the remote vehicle, vehicle transportation network informationcorresponding to the remote vehicle, legal or regulatory information, ora combination thereof.

In some embodiments, the blocking monitor 4200 may determine, or update,probabilities of availability continually or periodically. In someembodiments, one or more classes or types of external object may beidentified as preferentially blocking, and the expected path of apreferentially blocking external object may overlap, spatially andtemporally, the expected path of another preferentially blockingexternal object. For example, the expected path of a pedestrian mayoverlap with the expected path of another pedestrian. In someembodiments, one or more classes or types of external object may beidentified as deferentially blocking, and the expected path of adeferentially blocking external object may be blocked, such as impededor otherwise affected, by other external objects. For example, theexpected path for a remote vehicle may be blocked by another remotevehicle or by a pedestrian.

In some embodiments, the blocking monitor 4200 may identify expectedpaths for preferentially blocking external objects, such as pedestrians,and may identify expected paths for deferentially blocking externalobjects, such as remote vehicles, subject to the expected paths for thepreferentially blocking external objects. In some embodiments, theblocking monitor 4200 may communicate probabilities of availability, orcorresponding blocking probabilities, to the autonomous vehicleoperational management controller 4100. The autonomous vehicleoperational management controller 4100 may communicate the probabilitiesof availability, or corresponding blocking probabilities, to respectiveinstantiated instances of the scenario-specific operational controlevaluation modules 4400.

Each scenario-specific operational control evaluation module 4400 maymodel a respective distinct vehicle operational scenario. The autonomousvehicle operational management system 4000 may include any number ofscenario-specific operational control evaluation modules 4400, eachmodeling a respective distinct vehicle operational scenario.

In some embodiments, modeling a distinct vehicle operational scenario,by a scenario-specific operational control evaluation module 4400, mayinclude generating, maintaining, or both state information representingaspects of an operational environment of the autonomous vehiclecorresponding to the distinct vehicle operational scenario, identifyingpotential interactions among the modeled aspects respective of thecorresponding states, and determining a candidate vehicle control actionthat solves the model. In some embodiments, aspects of the operationalenvironment of the autonomous vehicle other than the defined set ofaspects of the operational environment of the autonomous vehiclecorresponding to the distinct vehicle operational scenario may beomitted from the model.

The autonomous vehicle operational management system 4000 may besolution independent and may include any model of a distinct vehicleoperational scenario, such as a single-agent model, a multi-agent model,a learning model, or any other model of one or more distinct vehicleoperational scenarios.

One or more of the scenario-specific operational control evaluationmodules 4400 may be a Classical Planning (CP) model, which may be asingle-agent model, and which may model a distinct vehicle operationalscenario based on a defined input state, which may indicate respectivenon-probabilistic states of the elements of the operational environmentof the autonomous vehicle for the distinct vehicle operational scenariomodeled by the scenario-specific operational control evaluation modules4400. In a Classical Planning model, one or more aspects, such asgeospatial location, of modeled elements, such as external objects,associated with a temporal location may differ from the correspondingaspects associated with another temporal location, such as animmediately subsequent temporal location, non-probabilistically, such asby a defined, or fixed, amount. For example, at a first temporallocation, a remote vehicle may have a first geospatial location, and, atan immediately subsequent second temporal location the remote vehiclemay have a second geospatial location that differs from the firstgeospatial location by a defined geospatial distances, such as a definednumber of meters, along an expected path for the remote vehicle.

One or more of the scenario-specific operational control evaluationmodules 4400 may be a discrete time stochastic control process, such asa Markov Decision Process (MDP) model, which may be a single-agentmodel, and which may model a distinct vehicle operational scenario basedon a defined input state. Changes to the operational environment of theautonomous vehicle, such as a change of location for an external object,may be modeled as probabilistic changes. A Markov Decision Process modelmay utilize more processing resources and may more accurately model thedistinct vehicle operational scenario than a Classical Planning (CP)model.

A Markov Decision Process model may model a distinct vehicle operationalscenario as a sequence of temporal locations, such as a current temporallocation, future temporal locations, or both, with corresponding states,such as a current state, expected future states, or both. At eachtemporal location the model may have a state, which may be an expectedstate, and which may be associated with one or more candidate vehiclecontrol actions. The model may represent the autonomous vehicle as anagent, which may transition, along the sequence of temporal locations,from one state (a current state) to another state (subsequent state) inaccordance with an identified action for the current state and aprobability that the identified action will transition the state fromthe current state to the subsequent state.

The model may accrue a reward, which may be a positive or negativevalue, corresponding to transitioning from the one state to anotheraccording to a respective action. The model may solve the distinctvehicle operational scenario by identifying the actions corresponding toeach state in the sequence of temporal locations that maximizes thecumulative reward. Solving a model may include identifying a vehiclecontrol action in response to the modeled scenario and the operationalenvironment information.

A Markov Decision Process model may model a distinct vehicle operationalscenario using a set of states, a set of actions, a set of statetransition probabilities, a reward function, or a combination thereof.In some embodiments, modeling a distinct vehicle operational scenariomay include using a discount factor, which may adjust, or discount, theoutput of the reward function applied to subsequent temporal periods.

The set of states may include a current state of the Markov DecisionProcess model, one or more possible subsequent states of the MarkovDecision Process model, or a combination thereof. A state may representan identified condition, which may be an expected condition, ofrespective defined aspects, such as external objects and traffic controldevices, of the operational environment of the autonomous vehicle thatmay probabilistically affect the operation of the autonomous vehicle ata discrete temporal location. For example, a remote vehicle operating inthe proximity of the autonomous vehicle may affect the operation of theautonomous vehicle and may be represented in a Markov Decision Processmodel, which may include representing an identified or expectedgeospatial location of the remote vehicle, an identified or expectedpath, heading, or both of the remote vehicle, an identified or expectedvelocity of the remote vehicle, an identified or expected accelerationor deceleration rate of the remote vehicle, or a combination thereofcorresponding to the respected temporal location. At instantiation, thecurrent state of the Markov Decision Process model may correspond to acontemporaneous state or condition of the operating environment. Arespective set of states may be defined for each distinct vehicleoperational scenario.

Although any number or cardinality of states may be used, the number orcardinality of states included in a model may be limited to a definedmaximum number of states, such as 300 states. For example, a model mayinclude the 300 most probable states for a corresponding scenario.

The set of actions may include vehicle control actions available to theMarkov Decision Process model at each state in the set of states. Arespective set of actions may be defined for each distinct vehicleoperational scenario.

The set of state transition probabilities may probabilisticallyrepresent potential or expected changes to the operational environmentof the autonomous vehicle, as represented by the states, responsive tothe actions. For example, a state transition probability may indicate aprobability that the operational environment of the autonomous vehiclecorresponds to a respective state at a respective temporal locationimmediately subsequent to a current temporal location corresponding to acurrent state in response to traversing the vehicle transportationnetwork by the autonomous vehicle from the current state in accordancewith a respective action.

The set of state transition probabilities may be identified based on theoperational environment information. For example, the operationalenvironment information may indicate an area type, such as urban orrural, a time of day, an ambient light level, weather conditions,traffic conditions, which may include expected traffic conditions, suchas rush hour conditions, event-related traffic congestion, or holidayrelated driver behavior conditions, road conditions, jurisdictionalconditions, such as country, state, or municipality conditions, or anyother condition or combination of conditions that may affect theoperation of the autonomous vehicle.

Examples of state transition probabilities associated with a pedestrianvehicle operational scenario may include a defined probability of apedestrian jaywalking, which may be based on a geospatial distancebetween the pedestrian and the respective road segment; a definedprobability of a pedestrian stopping in an intersection; a definedprobability of a pedestrian crossing at a crosswalk; a definedprobability of a pedestrian yielding to the autonomous vehicle at acrosswalk; any other probability associated with a pedestrian vehicleoperational scenario.

Examples of state transition probabilities associated with anintersection vehicle operational scenario may include a definedprobability of a remote vehicle arriving at an intersection; a definedprobability of a remote vehicle cutting-off the autonomous vehicle; adefined probability of a remote vehicle traversing an intersectionimmediately subsequent to, and in close proximity to, a second remotevehicle traversing the intersection, such as in the absence of aright-of-way (piggybacking); a defined probability of a remote vehiclestopping, adjacent to the intersection, in accordance with a trafficcontrol device, regulation, or other indication of right-of-way, priorto traversing the intersection; a defined probability of a remotevehicle traversing the intersection; a defined probability of a remotevehicle diverging from an expected path proximal to the intersection; adefined probability of a remote vehicle diverging from an expectedright-of-way priority; any other probability associated with a anintersection vehicle operational scenario.

Examples of state transition probabilities associated with a lane changevehicle operational scenario may include a defined probability of aremote vehicle changing velocity, such as a defined probability of aremote vehicle behind the autonomous vehicle increasing velocity or adefined probability of a remote vehicle in front of the autonomousvehicle decreasing velocity; a defined probability of a remote vehiclein front of the autonomous vehicle changing lanes; a defined probabilityof a remote vehicle proximate to the autonomous vehicle changing speedto allow the autonomous vehicle to merge into a lane; or any otherprobabilities associated with a lane change vehicle operationalscenario.

The reward function may determine a respective positive or negative(cost) value that may be accrued for each combination of state andaction, which may represent an expected value of the autonomous vehicletraversing the vehicle transportation network from the correspondingstate in accordance with the corresponding vehicle control action to thesubsequent state.

The reward function may be identified based on the operationalenvironment information. For example, the operational environmentinformation may indicate an area type, such as urban or rural, a time ofday, an ambient light level, weather conditions, traffic conditions,which may include expected traffic conditions, such as rush hourconditions, event-related traffic congestion, or holiday related driverbehavior conditions, road conditions, jurisdictional conditions, such ascountry, state, or municipality conditions, or any other condition orcombination of conditions that may affect the operation of theautonomous vehicle.

One or more of the scenario-specific operational control evaluationmodules 4400 may be a Partially Observable Markov Decision Process(POMDP) model, which may be a single-agent model. A Partially ObservableMarkov Decision Process model may be similar to a Markov DecisionProcess model, except that a Partially Observable Markov DecisionProcess model may include modeling uncertain states. A PartiallyObservable Markov Decision Process model may include modelingconfidence, sensor trustworthiness, distraction, noise, uncertainty,such as sensor uncertainty, or the like. A Partially Observable MarkovDecision Process model may utilize more processing resources and maymore accurately model the distinct vehicle operational scenario than aMarkov Decision Process model.

A Partially Observable Markov Decision Process model may model adistinct vehicle operational scenario using a set of states, a set ofstates, a set of actions, a set of state transition probabilities, areward function, a set of observations, a set of conditional observationprobabilities, or a combination thereof. The set of states, the set ofactions, the set of state transition probabilities, and the rewardfunction may be similar to those described above with respect to theMarkov Decision Process model.

The set of observations may include observations corresponding torespective states. An observation may provide information about theattributes of a respective state. An observation may correspond with arespective temporal location. An observation may include operationalenvironment information, such as sensor information. An observation mayinclude expected or predicted operational environment information.

For example, a Partially Observable Markov Decision Process model mayinclude an autonomous vehicle at a first geospatial location and firsttemporal location corresponding to a first state, the model may indicatethat the autonomous vehicle may identify and perform, or attempt toperform, a vehicle control action to traverse the vehicle transportationnetwork from the first geospatial location to a second geospatiallocation at a second temporal location immediately subsequent to thefirst temporal location, and the set of observations corresponding tothe second temporal location may include the operational environmentinformation that may be identified corresponding to the second temporallocation, such as geospatial location information for the autonomousvehicle, geospatial location information for one or more externalobjects, probabilities of availability, expected path information, orthe like.

The set of conditional observation probabilities may includeprobabilities of making respective observations based on the operationalenvironment of the autonomous vehicle. For example, an autonomousvehicle may approach an intersection by traversing a first road,contemporaneously, a remote vehicle may approach the intersection bytraversing a second road, the autonomous vehicle may identify andevaluate operational environment information, such as sensorinformation, corresponding to the intersection, which may includeoperational environment information corresponding to the remote vehicle.In some embodiments, the operational environment information may beinaccurate, incomplete, or erroneous. In a Markov Decision Processmodel, the autonomous vehicle may non-probabilistically identify theremote vehicle, which may include identifying a location of the remotevehicle, an expected path for the remote vehicle, or the like, and theidentified information, such as the identified location of the remotevehicle, based on inaccurate operational environment information, may beinaccurate or erroneous. In a Partially Observable Markov DecisionProcess model the autonomous vehicle may identify informationprobabilistically identifying the remote vehicle, which may includeprobabilistically identifying location information for the remotevehicle, such as location information indicating that the remote vehiclemay be proximate to the intersection. The conditional observationprobability corresponding to observing, or probabilisticallyidentifying, the location of the remote vehicle may represent theprobability that the identified operational environment informationaccurately represents the location of the remote vehicle.

The set of conditional observation probabilities may be identified basedon the operational environment information. For example, the operationalenvironment information may indicate an area type, such as urban orrural, a time of day, an ambient light level, weather conditions,traffic conditions, which may include expected traffic conditions, suchas rush hour conditions, event-related traffic congestion, or holidayrelated driver behavior conditions, road conditions, jurisdictionalconditions, such as country, state, or municipality conditions, or anyother condition or combination of conditions that may affect theoperation of the autonomous vehicle.

In some embodiments, such as embodiments implementing a PartiallyObservable Markov Decision Process model, modeling an autonomous vehicleoperational control scenario may include modeling occlusions. Forexample, the operational environment information may include informationcorresponding to one or more occlusions, such as sensor occlusions, inthe operational environment of the autonomous vehicle such that theoperational environment information may omit information representingone or more occluded external objects in the operational environment ofthe autonomous vehicle. For example, an occlusion may be an externalobject, such as a traffic signs, a building, a tree, an identifiedexternal object, or any other operational condition or combination ofoperational conditions capable of occluding one or more otheroperational conditions, such as external objects, from the autonomousvehicle at a defined spatiotemporal location. In some embodiments, anoperational environment monitor 4300 may identify occlusions, mayidentify or determine a probability that an external object is occluded,or hidden, by an identified occlusion, and may include occluded vehicleprobability information in the operational environment informationoutput to the autonomous vehicle operational management controller 4100,and communicated, by the autonomous vehicle operational managementcontroller 4100, to the respective scenario-specific operational controlevaluation modules 4400.

In some embodiments, one or more of the scenario-specific operationalcontrol evaluation modules 4400 may be a Decentralized PartiallyObservable Markov Decision Process (Dec-POMDP) model, which may be amulti-agent model, and which may model a distinct vehicle operationalscenario. A Decentralized Partially Observable Markov Decision Processmodel may be similar to a Partially Observable Markov Decision Processmodel except that a Partially Observable Markov Decision Process modelmay model the autonomous vehicle and a subset, such as one, of externalobjects and a Decentralized Partially Observable Markov Decision Processmodel may model the autonomous vehicle and the set of external objects.

In some embodiments, one or more of the scenario-specific operationalcontrol evaluation modules 4400 may be a Partially Observable StochasticGame (POSG) model, which may be a multi-agent model, and which may modela distinct vehicle operational scenario. A Partially ObservableStochastic Game model may be similar to a Decentralized PartiallyObservable Markov Decision Process except that the DecentralizedPartially Observable Markov Decision Process model may include a rewardfunction for the autonomous vehicle and the Partially ObservableStochastic Game model may include the reward function for the autonomousvehicle and a respective reward function for each external object.

In some embodiments, one or more of the scenario-specific operationalcontrol evaluation modules 4400 may be a Reinforcement Learning (RL)model, which may be a learning model, and which may model a distinctvehicle operational scenario. A Reinforcement Learning model may besimilar to a Markov Decision Process model or a Partially ObservableMarkov Decision Process model except that defined state transitionprobabilities, observation probabilities, reward function, or anycombination thereof, may be omitted from the model.

In some embodiments, a Reinforcement Learning model may be a model-basedReinforcement Learning model, which may include generating statetransition probabilities, observation probabilities, a reward function,or any combination thereof based on one or more modeled or observedevents.

In a Reinforcement Learning model, the model may evaluate one or moreevents or interactions, which may be simulated events, such astraversing an intersection, traversing a vehicle transportation networknear a pedestrian, or changing lanes, and may generate, or modify, acorresponding model, or a solution thereof, in response to therespective event. For example, the autonomous vehicle may traverse anintersection using a Reinforcement Learning model. The ReinforcementLearning model may indicate a candidate vehicle control action fortraversing the intersection. The autonomous vehicle may traverse theintersection using the candidate vehicle control action as the vehiclecontrol action for a temporal location. The autonomous vehicle maydetermine a result of traversing the intersection using the candidatevehicle control action, and may update the model based on the result.

In an example, at a first temporal location a remote vehicle may bestationary at an intersection with a prohibited right-of-way indication,such as a red light, the Reinforcement Learning model may indicate a‘proceed’ candidate vehicle control action for the first temporallocation, the Reinforcement Learning model may include a probability ofidentifying operational environment information at a subsequent temporallocation, subsequent to traversing the vehicle transportation network inaccordance with the identified candidate vehicle control action,indicating that a geospatial location of the remote vehiclecorresponding to the first temporal location differs from a geospatiallocation of the remote vehicle corresponding to the second temporallocation is low, such as 0/100. The autonomous vehicle may traverse thevehicle transportation network in accordance with the identifiedcandidate vehicle control action, may subsequently determine that thegeospatial location of the remote vehicle corresponding to the firsttemporal location differs from the geospatial location of the remotevehicle corresponding to the second temporal location, and may modify,or update, the probability accordingly incorporate the identified event,such as to 1/101.

In another example, the Reinforcement Learning model may indicate adefined positive expected reward for traversing the vehicletransportation network from a first temporal location to a secondtemporal location in accordance with an identified vehicle controlaction and in accordance with identified operational environmentinformation, which may be probabilistic. The autonomous vehicle maytraverse the vehicle transportation network in accordance with theidentified vehicle control action. The autonomous vehicle may determine,based on subsequently identified operational environment information,which may be probabilistic, that the operational environment informationcorresponding to the second temporal location is substantially similarto the operational environment information identified corresponding tothe first temporal location, which may indicate a cost, such as in time,of traversing the vehicle transportation network in accordance with theidentified vehicle control action, and the Reinforcement Learning modelmay reduce the corresponding expected reward.

The autonomous vehicle operational management system 4000 may includeany number or combination of types of models. For example, thepedestrian-scenario-specific operational control evaluation module 4410,the intersection-scenario-specific operational control evaluation module4420, and the lane change-scenario-specific operational controlevaluation module 4430 may be Partially Observable Markov DecisionProcess models. In another example, the pedestrian-scenario-specificoperational control evaluation module 4410 may be a Markov DecisionProcess model and the intersection-scenario-specific operational controlevaluation module 4420 and the lane change-scenario-specific operationalcontrol evaluation module 4430 may be Partially Observable MarkovDecision Process models.

The autonomous vehicle operational management controller 4100 mayinstantiate any number of instances of the scenario-specific operationalcontrol evaluation modules 4400 based on the operational environmentinformation.

For example, the operational environment information may includeinformation representing a pedestrian approaching an intersection alongan expected path for the autonomous vehicle, and the autonomous vehicleoperational management controller 4100 may identify a pedestrian vehicleoperational scenario, an intersection vehicle operational scenario, orboth. The autonomous vehicle operational management controller 4100 mayinstantiate an instance of the pedestrian-scenario-specific operationalcontrol evaluation module 4410, an instance of theintersection-scenario-specific operation control evaluation module 4420,or both.

In another example, the operational environment information may includeinformation representing more than one pedestrians at or near anintersection along an expected path for the autonomous vehicle. Theautonomous vehicle operational management controller 4100 may identifypedestrian operational scenarios corresponding to the one or morepedestrians, an intersection vehicle operational scenario, or acombination thereof. The autonomous vehicle operational managementcontroller 4100 may instantiate instances of thepedestrian-scenario-specific operational control evaluation module 4410for some or all of the pedestrian operational scenarios, an instance ofthe intersection-scenario-specific operation control evaluation module4420, or a combination thereof.

The pedestrian-scenario-specific operational control evaluation module4410 may be a model of an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network proximate to a pedestrian (pedestrianscenario). The pedestrian-scenario-specific operation control evaluationmodule 4410 may receive operational environment information, such as thepedestrian information generated by the operational environment monitor4310 for monitoring pedestrians, from the autonomous vehicle operationalmanagement controller 4100.

The pedestrian-scenario-specific operational control evaluation module4410 may model pedestrian behavior corresponding to the pedestriantraversing a portion of the vehicle transportation network or otherwiseprobabilistically affecting the operation of the autonomous vehicle. Insome embodiments, the pedestrian-scenario-specific operational controlevaluation module 4410 may model a pedestrian as acting in accordancewith pedestrian model rules expressing probable pedestrian behavior. Forexample, the pedestrian model rules may express vehicle transportationnetwork regulations, pedestrian transportation network regulations,predicted pedestrian behavior, societal norms, or a combination thereof.For example, the pedestrian model rules may indicate a probability thata pedestrian may traverse a portion of the vehicle transportationnetwork via a crosswalk or other defined pedestrian access area. In someembodiments, the pedestrian-scenario-specific operational controlevaluation module 4410 may model a pedestrian as acting independently ofdefined vehicle transportation network regulations, pedestriantransportation network regulations, or both, such as by jaywalking.

The pedestrian-scenario-specific operational control evaluation module4410 may output a candidate vehicle control action, such as a ‘stop’candidate vehicle control action, an ‘advance’ candidate vehicle controlaction, or a ‘proceed’ candidate vehicle control action. In someembodiments, the candidate vehicle control action may be a compoundvehicle control action. For example, the candidate vehicle controlaction may include an ‘advance’ vehicle control action, which may be anindirect signaling pedestrian communication vehicle control action, andmay include a direct signaling pedestrian communication vehicle controlaction, such as flashing headlights of the autonomous vehicle orsounding a horn of the autonomous vehicle. An example of an autonomousvehicle operational control scenario that includes the autonomousvehicle traversing a portion of the vehicle transportation networkproximate to a pedestrian is shown in FIG. 7.

The intersection-scenario-specific operational control evaluation module4420 may be a model of an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network that includes an intersection. Theintersection-scenario-specific operational control evaluation module4420 may model the behavior of remote vehicles traversing anintersection in the vehicle transportation network or otherwiseprobabilistically affecting the operation of the autonomous vehicletraversing the intersection. An intersection may include any portion ofthe vehicle transportation network wherein a vehicle may transfer fromone road to another.

In some embodiments, modeling an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network that includes an intersection mayinclude determining a right-of-way order for vehicles to traverse theintersection, such as by negotiating with remote vehicles.

In some embodiments, modeling an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network that includes an intersection mayinclude modeling one or more traffic controls, such as a stop sign, ayield sign, a traffic light, or any other traffic control device,regulation, signal, or combination thereof.

In some embodiments, modeling an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network that includes an intersection mayinclude outputting an ‘advance’ candidate vehicle control action,receiving information, such as sensor information, in response to theautonomous vehicle performing the ‘advance’ candidate vehicle controlaction, and outputting a subsequent candidate vehicle control actionbased on the received information.

In some embodiments, modeling an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network that includes an intersection mayinclude modeling a probability that a remote vehicle may traverse theintersection in accordance with vehicle transportation networkregulations. In some embodiments, modeling an autonomous vehicleoperational control scenario that includes the autonomous vehicletraversing a portion of the vehicle transportation network that includesan intersection may include modeling a probability that a remote vehiclemay traverse the intersection independent of one or more vehicletransportation network regulations, such as by following closely behindor piggybacking another remote vehicle having a right-of-way.

The intersection-scenario-specific operational control evaluation module4420 may output a candidate vehicle control action, such as a ‘stop’candidate vehicle control action, an ‘advance’ candidate vehicle controlaction, or a ‘proceed’ candidate vehicle control action. In someembodiments, the candidate vehicle control action may be a compoundvehicle control action. For example, the candidate vehicle controlaction may include a ‘proceed’ vehicle control action and a signalingcommunication vehicle control action, such as flashing a turn signal ofthe autonomous vehicle. An example of an autonomous vehicle operationalcontrol scenario that includes the autonomous vehicle traversing anintersection is shown in FIG. 8.

The lane change-scenario-specific operational control evaluation module4430 may be a model of an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network by performing a lane changeoperation. The lane change-scenario-specific operational controlevaluation module 4430 may model the behavior of remote vehiclesprobabilistically affecting the operation of the autonomous vehicletraversing the lane change.

In some embodiments, modeling an autonomous vehicle operational controlscenario that includes the autonomous vehicle traversing a portion ofthe vehicle transportation network by performing a lane change mayinclude outputting ‘maintain’ candidate vehicle control action, a‘proceed’ vehicle control action, an ‘accelerate’ vehicle controlaction, a ‘decelerate’ vehicle control action, or a combination thereof.An example of an autonomous vehicle operational control scenario thatincludes the autonomous vehicle changing lanes is shown in FIG. 9.

In some embodiments, one or more of the autonomous vehicle operationalmanagement controller 4100, the blocking monitor 4200, the operationalenvironment monitors 4300, or the scenario-specific operation controlevaluation modules 4400, may operate continuously or periodically, suchas at a frequency of ten hertz (10 Hz). For example, the autonomousvehicle operational management controller 4100 may identify a vehiclecontrol action many times, such as ten times, per second. Theoperational frequency of each component of the autonomous vehicleoperational management system 4000 may be synchronized orunsynchronized, and the operational rate of one or more of theautonomous vehicle operational management controller 4100, the blockingmonitor 4200, the operational environment monitors 4300, or thescenario-specific operation control evaluation modules 4400 may beindependent of the operational rate of another one or more of theautonomous vehicle operational management controller 4100, the blockingmonitor 4200, the operational environment monitors 4300, or thescenario-specific operation control evaluation modules 4400.

In some embodiments, the candidate vehicle control actions output by theinstances of the scenario-specific operation control evaluation modules4400 may include, or be associated with, operational environmentinformation, such as state information, temporal information, or both.For example, a candidate vehicle control action may be associated withoperational environment information representing a possible futurestate, a future temporal location, or both. In some embodiments, theautonomous vehicle operational management controller 4100 may identifystale candidate vehicle control actions representing past temporallocations, states having a probability of occurrence below a minimumthreshold, or unelected candidate vehicle control actions, and maydelete, omit, or ignore the stale candidate vehicle control actions.

FIG. 5 is a flow diagram of an example of an autonomous vehicleoperational management 5000 in accordance with embodiments of thisdisclosure. Autonomous vehicle operational management 5000 may beimplemented in an autonomous vehicle, such as the vehicle 1000 shown inFIG. 1, one of the vehicles 2100/2110 shown in FIG. 2, a semi-autonomousvehicle, or any other vehicle implementing autonomous driving. Forexample, an autonomous vehicle may implement an autonomous vehicleoperational management system, such as the autonomous vehicleoperational management system 4000 shown in FIG. 4.

Autonomous vehicle operational management 5000 may include implementingor operating one or more modules or components, which may includeoperating an autonomous vehicle operational management controller orexecutor 5100, such as the autonomous vehicle operational managementcontroller 4100 shown in FIG. 4; a blocking monitor 5200, such as theblocking monitor, 4200 shown in FIG. 4; zero or more scenario-specificoperational control evaluation module instances (SSOCEMI) 5300, such asinstances of the scenario-specific operational control evaluationmodules 4400 shown in FIG. 4; or a combination thereof.

Although not shown separately in FIG. 5, in some embodiments, theexecutor 5100 may monitor the operational environment of the autonomousvehicle, or defined aspects thereof. In some embodiments, monitoring theoperational environment of the autonomous vehicle may includeidentifying and tracking external objects at 5110, identifying distinctvehicle operational scenarios at 5120, or a combination thereof.

The executor 5100 may identify an operational environment, or an aspectthereof, of the autonomous vehicle at 5110. Identifying the operationalenvironment may include identifying operational environment informationrepresenting the operational environment, or one or more aspectsthereof. In some embodiments, the operational environment informationmay include vehicle information for the autonomous vehicle, informationrepresenting the vehicle transportation network, or one or more aspectsthereof, proximate to the autonomous vehicle, information representingexternal objects, or one or more aspects thereof, within the operationalenvironment of the autonomous vehicle, or a combination thereof.

In some embodiments, the executor 5100 may identify the operationalenvironment information at 5110 based on sensor information, vehicletransportation network information, previously identified operationalenvironment information, or any other information or combination ofinformation describing an aspect or aspects of the operationalenvironment. In some embodiments, the sensor information may beprocessed sensor information, such as processed sensor information froma sensor information processing unit of the autonomous vehicle, whichmay receive sensor information from the sensor of the autonomous vehicleand may generate the processed sensor information based on the sensorinformation.

In some embodiments, identifying the operational environment informationat 5110 may include receiving information indicating one or more aspectsof the operational environment from a sensor of the autonomous vehicle,such as the sensor 1360 shown in FIG. 1 or the on-vehicle sensors 2105shown in FIG. 2. For example, the sensor may detect an external object,such as a pedestrian, a vehicle, or any other object, external to theautonomous vehicle, within a defined distance, such as 300 meters, ofthe autonomous vehicle, and the sensor may send sensor informationindicating or representing the external object to the executor 5100. Insome embodiments, the sensor, or another unit of the autonomous vehicle,may store the sensor information in a memory, such as the memory 1340shown in FIG. 1, of the autonomous vehicle and the autonomous vehicleoperational management controller 5100 reading the sensor informationfrom the memory.

In some embodiments, the external object indicated by the sensorinformation may be indeterminate, and the autonomous vehicle operationalmanagement controller 5100 may identify object information, such as anobject type, based on the sensor information, other information, such asinformation from another sensor, information corresponding to apreviously identified object, or a combination thereof. In someembodiments, the sensor, or another unit of the autonomous vehicle mayidentify the object information and may send the object identificationinformation to the autonomous vehicle operational management controller5100.

In some embodiments, the sensor information may indicate a roadcondition, a road feature, or a combination thereof. For example, thesensor information may indicate a road condition, such as a wet roadcondition, an icy road condition, or any other road condition orconditions. In another example, the sensor information may indicate roadmarkings, such as a lane line, an aspect of roadway geometry, or anyother road feature or features.

In some embodiments, identifying the operational environment informationat 5110 may include identifying information indicating one or moreaspects of the operational environment from vehicle transportationnetwork information. For example, the autonomous vehicle operationalmanagement controller 5100 may read, or otherwise receive, vehicletransportation network information indicating that the autonomousvehicle is approaching an intersection, or otherwise describing ageometry or configuration of the vehicle transportation networkproximate to the autonomous vehicle, such as within 300 meters of theautonomous vehicle.

In some embodiments, identifying the operational environment informationat 5110 may include identifying information indicating one or moreaspects of the operational environment from a remote vehicle or otherremote device external to the autonomous vehicle. For example, theautonomous vehicle may receive, from a remote vehicle, via a wirelesselectronic communication link, a remote vehicle message including remotevehicle information indicating remote vehicle geospatial stateinformation for the remote vehicle, remote vehicle kinematic stateinformation for the remote vehicle, or both.

In some embodiments, the executor 5100 may include one or morescenario-specific monitor module instances. For example, the executor5100 may include a scenario-specific monitor module instance formonitoring pedestrians, a scenario-specific monitor module instance formonitoring intersections, a scenario-specific monitor module instancefor monitoring lane changes, or a combination thereof. Eachscenario-specific monitor module instance may receive, or otherwiseaccess, operational environment information corresponding to therespective scenario, and may send, store, or otherwise output to, or foraccess by, the executor 5100, the blocking monitor 5200, thescenario-specific operational control evaluation module instance 5300,or a combination thereof specialized monitor information correspondingto the respective scenario.

In some embodiments, the executor 5100 may send the operationalenvironment information representing an operational environment for theautonomous vehicle to the blocking monitor 5200 at 5112. Alternatively,or in addition, the blocking monitor 5200 may receive the operationalenvironment information representing an operational environment for theautonomous vehicle from another component of the autonomous vehicle,such as from a sensor of the autonomous vehicle, the blocking monitor5200 may read the operational environment information representing anoperational environment for the autonomous vehicle from a memory of theautonomous vehicle, or a combination thereof.

The executor 5100 may detect or identify one or more distinct vehicleoperational scenarios at 5120. In some embodiments, the executor 5100may detect distinct vehicle operational scenarios at 5120 based on oneor more aspects of the operational environment represented by theoperational environment information identified at 5110.

In some embodiments, the executor 5100 may identify multiple distinctvehicle operational scenarios, which may be aspects of a compoundvehicle operational scenario, at 5120. For example, the operationalenvironment information may include information representing apedestrian approaching an intersection along an expected path for theautonomous vehicle, and the executor 5100 may identify a pedestrianvehicle operational scenario, an intersection vehicle operationalscenario, or both at 5120. In another example, the operationalenvironment represented by the operational environment information mayinclude multiple external objects and the executor 5100 may identify adistinct vehicle operational scenario corresponding to each externalobject at 5120.

The executor 5100 may instantiate a scenario-specific operationalcontrol evaluation module instance 5300 based on one or more aspects ofthe operational environment represented by the operational environmentinformation at 5130. For example, the executor 5100 may instantiate thescenario-specific operational control evaluation module instance 5300 at5130 in response to identifying a distinct vehicle operational scenarioat 5120.

Although one scenario-specific operational control evaluation moduleinstance 5300 is shown in FIG. 5, the executor 5100 may instantiatemultiple scenario-specific operational control evaluation moduleinstances 5300 based on one or more aspects of the operationalenvironment represented by the operational environment informationidentified at 5110, each scenario-specific operational controlevaluation module instances 5300 corresponding to a respective distinctvehicle operational scenario detected at 5120, or a combination of adistinct external object identified at 5110 and a respective distinctvehicle operational scenario detected at 5120.

For example, the operational environment represented by the operationalenvironment information identified at 5110 may include multiple externalobjects, the executor 5100 may detect multiple distinct vehicleoperational scenarios, which may be aspects of a compound vehicleoperational scenario, at 5120 based on the operational environmentrepresented by the operational environment information identified at5110, and the executor 5100 may instantiate a scenario-specificoperational control evaluation module instance 5300 corresponding toeach distinct combination of a distinct vehicle operational scenario andan external object.

In some embodiments, a scenario-specific operational control evaluationmodule corresponding to the distinct vehicle operational scenarioidentified at 5120 may be unavailable and instantiating ascenario-specific operational control evaluation module instance 5300 at5130 may include generating, solving, and instantiating an instance 5300of a scenario-specific operational control evaluation modulecorresponding to the distinct vehicle operational scenario identified at5120. For example, the distinct vehicle operational scenario identifiedat 5120 may indicate an intersection including two lanes having stoptraffic control signals, such as stop signs, and two lanes having yieldtraffic control signals, such as yield signs, the availablescenario-specific operational control evaluation modules may include aPartially Observable Markov Decision Process scenario-specificoperational control evaluation module that differs from the distinctvehicle operational scenario identified at 5120, such as a PartiallyObservable Markov Decision Process scenario-specific operational controlevaluation module that models an intersection scenario including fourlanes having stop traffic control signals, and the executor 5100 maygenerate, solve, and instantiate an instance 5300 of a Markov DecisionProcess scenario-specific operational control evaluation module modelingan intersection including two lanes having stop traffic control signalsand two lanes having yield traffic control signals at 5130.

In some embodiments, instantiating a scenario-specific operationalcontrol evaluation module instance at 5130 may include identifying aconvergence probability of spatio-temporal convergence based oninformation about the autonomous vehicle, the operational environmentinformation, or a combination thereof. Identifying a convergenceprobability of spatio-temporal convergence may include identifying anexpected path for the autonomous vehicle, identifying an expected pathfor the remote vehicle, and identifying a probability of convergence forthe autonomous vehicle and the remote vehicle indicating a probabilitythat the autonomous vehicle and the remote vehicle may converge orcollide based on the expected path information. The scenario-specificoperational control evaluation module instance may be instantiated inresponse to determining that the convergence probability exceeds adefined threshold, such as a defined maximum acceptable convergenceprobability.

In some embodiments, instantiating a scenario-specific operationalcontrol evaluation module instances 5300 at 5130 may include sending theoperational environment information representing an operationalenvironment for the autonomous vehicle to the scenario-specificoperational control evaluation module instances 5300 as indicated at5132.

The scenario-specific operational control evaluation module instance5300 may receive the operational environment information representing anoperational environment for the autonomous vehicle, or one or moreaspects thereof, at 5310. For example, the scenario-specific operationalcontrol evaluation module instance 5300 may receive the operationalenvironment information representing an operational environment for theautonomous vehicle, or one or more aspects thereof, sent by the executor5100 at 5132. Alternatively, or in addition, the scenario-specificoperational control evaluation module instances 5300 may receive theoperational environment information representing an operationalenvironment for the autonomous vehicle from another component of theautonomous vehicle, such as from a sensor of the autonomous vehicle orfrom the blocking monitor 5200, the scenario-specific operationalcontrol evaluation module instances 5300 may read the operationalenvironment information representing an operational environment for theautonomous vehicle from a memory of the autonomous vehicle, or acombination thereof.

The blocking monitor 5200 may receive the operational environmentinformation representing an operational environment, or an aspectthereof, for the autonomous vehicle at 5210. For example, the blockingmonitor 5200 may receive the operational environment information, or anaspect thereof, sent by the executor 5100 at 5112. In some embodiments,the blocking monitor 5200 may receive the operational environmentinformation, or an aspect thereof, from a sensor of the autonomousvehicle, from an external device, such as a remote vehicle or aninfrastructure device, or a combination thereof. In some embodiments,the blocking monitor 5200 may read the operational environmentinformation, or an aspect thereof, from a memory, such as a memory ofthe autonomous vehicle.

The blocking monitor 5200 may determine a respective probability ofavailability (POA), or corresponding blocking probability, at 5220 forone or more portions of the vehicle transportation network, such asportions of the vehicle transportation network proximal to theautonomous vehicle, which may include portions of the vehicletransportation network corresponding to an expected path of theautonomous vehicle, such as an expected path identified based on acurrent route of the autonomous vehicle.

In some embodiments, determining the respective probability ofavailability at 5220 may include identifying external objects, trackingexternal objects, projecting location information for external objects,projecting path information for external objects, or a combinationthereof. For example, the blocking monitor 5200 may identify an externalobject and may identify an expected path for the external object, whichmay indicate a sequence of expected spatial locations, expected temporallocations, and corresponding probabilities.

In some embodiments, the blocking monitor 5200 may identify the expectedpath for an external object based on operational environmentinformation, such as information indicating a current location of theexternal object, information indicating a current trajectory for theexternal object, information indicating a type of classification of theexternal object, such as information classifying the external object asa pedestrian or a remote vehicle, vehicle transportation networkinformation, such as information indicating that the vehicletransportation network includes a crosswalk proximate to the externalobject, previously identified or tracked information associated with theexternal object, or any combination thereof. For example, the externalobject may be identified as a remote vehicle, and the expected path forthe remote vehicle may be identified based on information indicating acurrent location of the remote vehicle, information indicating a currenttrajectory of the remote vehicle, information indicating a current speedof the remote vehicle, vehicle transportation network informationcorresponding to the remote vehicle, legal or regulatory information, ora combination thereof.

In some embodiments, the blocking monitor 5200 may send theprobabilities of availability identified at 5220 to thescenario-specific operational control evaluation module instances 5300at 5222. Alternatively, or in addition, the blocking monitor 5200 maystore the probabilities of availability identified at 5220 in a memoryof the autonomous vehicle, or a combination thereof. Although notexpressly shown in FIG. 5, the blocking monitor 5200 may send theprobabilities of availability identified at 5220 to the executor 5100 at5212 in addition to, or in alternative to, sending the probabilities ofavailability to the scenario-specific operational control evaluationmodule instances 5300.

The scenario-specific operational control evaluation module instance5300 may receive the probabilities of availability at 5320. For example,the scenario-specific operational control evaluation module instance5300 may receive the probabilities of availability sent by the blockingmonitor 5200 at 5222. In some embodiments, the scenario-specificoperational control evaluation module instance 5300 may read theprobabilities of availability from a memory, such as a memory of theautonomous vehicle.

The scenario-specific operational control evaluation module instance5300 may solve a model of the corresponding distinct vehicle operationalscenario at 5330. In some embodiments, scenario-specific operationalcontrol evaluation module instance 5300 may generate or identify acandidate vehicle control action at 5330.

In some embodiments, the scenario-specific operational controlevaluation module instance 5300 may send the candidate vehicle controlaction identified at 5330 to the executor 5100 at 5332. Alternatively,or in addition, the scenario-specific operational control evaluationmodule instance 5300 may store the candidate vehicle control actionidentified at 5330 in a memory of the autonomous vehicle.

The executor 5100 may receive a candidate vehicle control action at5140. For example, the executor 5100 may receive the candidate vehiclecontrol action from the scenario-specific operational control evaluationmodule instance 5300 at 5140. Alternatively, or in addition, theexecutor 5100 may read the candidate vehicle control action from amemory of the autonomous vehicle.

The executor 5100 may approve the candidate vehicle control action, orotherwise identify the candidate vehicle control action as a vehiclecontrol action for controlling the autonomous vehicle to traverse thevehicle transportation network, at 5150. For example, the executor 5100may identify one distinct vehicle operational scenario at 5120,instantiate one scenario-specific operational control evaluation moduleinstance 5300 at 5130, receive one candidate vehicle control action at5140, and may approve the candidate vehicle control action at 5150.

In some embodiments, the executor 5100 may identify multiple distinctvehicle operational scenarios at 5120, instantiate multiplescenario-specific operational control evaluation module instances 5300at 5130, receive multiple candidate vehicle control actions at 5140, andmay approve one or more of the candidate vehicle control actions at5150. In addition, or in the alternative, autonomous vehicle operationalmanagement 5000 may include operating one or more previouslyinstantiated scenario-specific operational control evaluation moduleinstances (not expressly shown), and the executor may receive candidatevehicle control actions at 5140 from the scenario-specific operationalcontrol evaluation module instance instantiated at 5130 and from one ormore of the previously instantiated scenario-specific operationalcontrol evaluation module instances, and may approve one or more of thecandidate vehicle control actions at 5150.

Approving a candidate vehicle control action at 5150 may includedetermining whether to traverse a portion of the vehicle transportationnetwork in accordance with the candidate vehicle control action.

The executor 5100 may control the autonomous vehicle to traverse thevehicle transportation network, or a portion thereof, at 5160 inaccordance with the vehicle control action identified at 5150.

The executor 5100 may identify an operational environment, or an aspectthereof, of the autonomous vehicle at 5170. Identifying an operationalenvironment, or an aspect thereof, of the autonomous vehicle at 5170 maybe similar to identifying the operational environment of the autonomousvehicle at 5110 and may include updating previously identifiedoperational environment information.

The executor 5100 may determine or detect whether a distinct vehicleoperational scenario is resolved or unresolved at 5180. For example, theexecutor 5100 may receive operation environment information continuouslyor on a periodic basis, as described above. The executor 5100 mayevaluate the operational environment information to determine whetherthe distinct vehicle operational scenario has resolved.

In some embodiments, the executor 5100 may determine that the distinctvehicle operational scenario corresponding to the scenario-specificoperational control evaluation module instance 5300 is unresolved at5180, the executor 5100 may send the operational environment informationidentified at 5170 to the scenario-specific operational controlevaluation module instances 5300 as indicated at 5185, anduninstantiating the scenario-specific operational control evaluationmodule instance 5300 at 5180 may be omitted or differed.

In some embodiments, the executor 5100 may determine that the distinctvehicle operational scenario is resolved at 5180 and may uninstantiateat 5190 the scenario-specific operational control evaluation moduleinstances 5300 corresponding to the distinct vehicle operationalscenario determined to be resolved at 5180. For example, the executor5100 may identify a distinct set of operative conditions forming thedistinct vehicle operational scenario for the autonomous vehicle at5120, may determine that one or more of the operative conditions hasexpired, or has a probability of affecting the operation of theautonomous vehicle below a defined threshold at 5180, and mayuninstantiate the corresponding scenario-specific operational controlevaluation module instance 5300.

Although not expressly shown in FIG. 5, the executor 5100 maycontinuously or periodically repeat identifying or updating theoperational environment information at 5170, determining whether thedistinct vehicle operational scenario is resolved at 5180, and, inresponse to determining that the distinct vehicle operational scenariois unresolved at 5180, sending the operational environment informationidentified at 5170 to the scenario-specific operational controlevaluation module instances 5300 as indicated at 5185, until determiningwhether the distinct vehicle operational scenario is resolved at 5180includes determining that the distinct vehicle operational scenario isresolved.

FIG. 6 is a diagram of an example of a blocking scene 6000 in accordancewith embodiments of this disclosure. Autonomous vehicle operationalmanagement, such as the autonomous vehicle operational management 5000shown in FIG. 5, may include an autonomous vehicle 6100, such as thevehicle 1000 shown in FIG. 1, one of the vehicles 2100/2110 shown inFIG. 2, a semi-autonomous vehicle, or any other vehicle implementingautonomous driving, operating an autonomous vehicle operationalmanagement system, such as the autonomous vehicle operational managementsystem 4000 shown in FIG. 4 including a blocking monitor, such as theblocking monitor 4200 shown in FIG. 4 or the blocking monitor 5200 shownin FIG. 5, to determine a probability of availability, or acorresponding blocking probability, for a portion or an area of avehicle transportation network corresponding to the blocking scene 6000.The blocking monitor may operate, and probabilities of availability maybe determined, in conjunction with, or independent of, definedautonomous vehicle operational control scenarios.

The portion of the vehicle transportation network corresponding to theblocking scene 6000 shown in FIG. 6 includes the autonomous vehicle 6100traversing a first road 6200, approaching an intersection 6210 with asecond road 6220. The intersection 6210 includes a crosswalk 6300. Apedestrian 6400 is approaching the crosswalk 6300. A remote vehicle 6500is traversing the second road 6220 approaching the intersection 6210. Anexpected path 6110 for the autonomous vehicle 6100 indicates that theautonomous vehicle 6100 may traverse the intersection 6210 by turningright from the first road 6200 to the second road 6220. An alternativeexpected path 6120 for the autonomous vehicle 6100, shown using a brokenline, indicates that the autonomous vehicle 6100 may traverse theintersection 6210 by turning left from the first road 6200 to the secondroad 6220.

The blocking monitor may identify an expected path 6410 for thepedestrian 6400. For example, sensor information may indicate that thepedestrian 6400 has a speed exceeding a threshold and a trajectoryintersecting the crosswalk 6300, vehicle transportation networkinformation may indicate that the intersection includes regulatorycontrols such that traversing the intersection in accordance with theregulatory controls by the vehicles yielding to pedestrians in thecrosswalk, or the intersection 6210 may include one or more trafficcontrol devices (not shown) indicating a permitted right-of-way signalfor the pedestrian 6400, and the expected path 6410 for the pedestrian6400 may be identified as including the pedestrian 6400 traversing thecrosswalk 6300 with a high probability, such as 1.0 or 100%.

The blocking monitor may identify expected paths 6510, 6520 for theremote vehicle 6500. For example, sensor information may indicate thatthe remote vehicle 6500 is approaching the intersection 6210, vehicletransportation network information may indicate that the remote vehicle6500 may traverse straight through the intersection 6210 or may turnright at the intersection 6210 onto the first road 6200, and theblocking monitor may identify a first expected path 6510 straightthrough the intersection, and a second expected path 6520 turning rightthrough the intersection for the remote vehicle 6500.

In some embodiments, the blocking monitor may identify a probability foreach of the expected paths 6510, 6520 based on, for example, operatinginformation for the remote vehicle 6500. For example, the operatinginformation for the remote vehicle 6500 may indicate a speed for theremote vehicle that exceeds a maximum turning threshold, and the firstexpected path 6510 may be identified with a high probability, such as0.9 or 90%, and the second expected path 6520 may be identified with alow probability, such as 0.1 or 10%.

In another example, the operating information for the remote vehicle6500 may indicate a speed for the remote vehicle that is within themaximum turning threshold, and the first expected path 6510 may beidentified with a low probability, such as 0.1 or 10%, and the secondexpected path 6520 may be identified with a high probability, such as0.9 or 90%.

The blocking monitor may identify a probability of availability for theportion or area of the second road 6220 proximate to, such as within afew, such as three, feet, of the expected path 6410 of the pedestrian,which may correspond with the crosswalk 6300, as low, such as 0%,indicating that the corresponding portion of the second road 6220 isblocked for a temporal period corresponding to the pedestrian 6400traversing the crosswalk 6300.

The blocking monitor may determine that the first expected path 6510 forthe remote vehicle 6500 and the expected path of the autonomous vehicle6100 are blocked by the pedestrian concurrent with the temporal periodcorresponding to the pedestrian 6400 traversing the crosswalk 6300.

FIG. 7 is a diagram of an example of a pedestrian scene 7000 includingpedestrian scenarios in accordance with embodiments of this disclosure.Autonomous vehicle operational management, such as the autonomousvehicle operational management 5000 shown in FIG. 5, may include anautonomous vehicle 7100, such as the vehicle 1000 shown in FIG. 1, oneof the vehicles 2100/2110 shown in FIG. 2, a semi-autonomous vehicle, orany other vehicle implementing autonomous driving, operating anautonomous vehicle operational management system, such as the autonomousvehicle operational management system 4000 shown in FIG. 4, including apedestrian-scenario-specific operational control evaluation moduleinstance, which may be an instance of a pedestrian-scenario-specificoperational control evaluation module, such as thepedestrian-scenario-specific operational control evaluation module 4410shown in FIG. 4, which may be a model of an autonomous vehicleoperational control scenario that includes the autonomous vehicle 7100traversing a portion of the vehicle transportation network proximate toa pedestrian. For simplicity and clarity, the portion of the vehicletransportation network corresponding to the pedestrian scene 7000 shownin FIG. 7 is oriented with north at the top and east at the right.

The portion of the vehicle transportation network corresponding to thepedestrian scene 7000 shown in FIG. 7 includes the autonomous vehicle7100 traversing northward along a road segment in a lane of a first road7200, approaching an intersection 7210 with a second road 7220. Theintersection 7210 includes a first crosswalk 7300 across the first road7200, and a second crosswalk 7310 across the second road 7220. A firstpedestrian 7400 is in the first road 7200 moving east at anon-pedestrian access area (jaywalking). A second pedestrian 7410 isproximal to the first crosswalk 7300 and is moving west-northwest. Athird pedestrian 7420 is approaching the first crosswalk 7300 from thewest. A fourth pedestrian 7430 is approaching the second crosswalk 7310from the north.

The autonomous vehicle operational management system may include anautonomous vehicle operational management controller, such as theautonomous vehicle operational management controller 4100 shown in FIG.4 or the executor 5100 shown in FIG. 5, and a blocking monitor, such asthe blocking monitor 4200 shown in FIG. 4 or the blocking monitor 5200shown in FIG. 5. The autonomous vehicle 7100 may include one or moresensors, one or more operational environment monitors, or a combinationthereof.

In some embodiments, the autonomous vehicle operational managementsystem may operate continuously or periodically, such as at eachtemporal location in a sequence of temporal locations. For simplicityand clarity, the geospatial location of the autonomous vehicle 7100, thefirst pedestrian 7400, the second pedestrian 7410, the third pedestrian7420, and the fourth pedestrian 7430 is shown in accordance with afirst, sequentially earliest, temporal location from the sequence oftemporal locations. Although described with reference to a sequence oftemporal locations for simplicity and clarity, each unit of theautonomous vehicle operational management system may operate at anyfrequency, the operation of respective units may be synchronized orunsynchronized, and operations may be performed concurrently with one ormore portions of one or more temporal locations. For simplicity andclarity, respective descriptions of one or more temporal locations, suchas temporal locations between the temporal locations described herein,may be omitted from this disclosure.

At one or more temporal location, such as at each temporal location, thesensors of the autonomous vehicle 7100 may detect informationcorresponding to the operational environment of the autonomous vehicle7100, such as information corresponding to one or more of thepedestrians 7400, 7410, 7420, 7430.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management system may identify anexpected path 7500 for the autonomous vehicle 7100, a route 7510 for theautonomous vehicle 7100, or both. In accordance with the first temporallocation, the expected path 7500 for the autonomous vehicle 7100indicates that the autonomous vehicle 7100 may traverse the intersection7210 by proceeding north along the first road 7200. The route 7510 forthe autonomous vehicle 7100 indicates that the autonomous vehicle 7100may turn right onto the second road 7220.

At one or more temporal location, such as at each temporal location, theoperational environment monitors of the autonomous vehicle 7100 mayidentify or generate operational environment information representing anoperational environment, or an aspect thereof, of the autonomous vehicle7100, such as in response to receiving sensor information correspondingto the pedestrians 7400, 7410, 7420, which may include associating thesensor information with the pedestrians 7400, 7410, 7420, 7430, and mayoutput the operational environment information, which may includeinformation representing the pedestrians 7400, 7410, 7420, 7430, to theautonomous vehicle operational management controller.

At one or more temporal location, such as at each temporal location, theblocking monitor may generate probability of availability informationindicating respective probabilities of availability for one or moreareas or portions of the vehicle transportation network. For example, inaccordance with the first temporal location, the blocking monitor maydetermine an expected path 7520 for the first pedestrian 7400 and aprobability of availability for an area or a portion of the vehicletransportation network proximate to a point of convergence between theexpected path 7520 for the first pedestrian 7400 and the expected path7500, or the route 7510, for the autonomous vehicle 7100.

In another example, the blocking monitor may determine an expected path7530 for the second pedestrian 7410, an expected path 7540 for the thirdpedestrian 7420, and a probability of availability for an area or aportion of the vehicle transportation network proximate to the firstcrosswalk 7300. Identifying the probability of availability for the areaor portion of the vehicle transportation network proximate to the firstcrosswalk 7300 may include identifying the second pedestrian 7410 andthe third pedestrian 7420 as preferentially blocking external objectsand determining that the corresponding expected paths 7530, 7540 mayoverlap spatially and temporally.

In another example, the blocking monitor may determine multiple expectedpaths for one or more external objects. For example, the blockingmonitor may identify a first expected path 7530 for the secondpedestrian 7410 with a high probability and may identify a secondexpected path 7532 for the second pedestrian 7410 with a lowprobability.

In another example, the blocking monitor may determine an expected path7550 for the fourth pedestrian 7430 and a probability of availabilityfor an area or a portion of the vehicle transportation network proximateto the second crosswalk 7310.

In some embodiments, generating the probability of availabilityinformation may include generating probabilities of availability for arespective area or portion of the vehicle transportation networkcorresponding to multiple temporal locations from the sequence oftemporal locations. The blocking monitor may output the probability ofavailability information to, or for access by, the autonomous vehicleoperational management controller.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may generateoperational environment information, or update previously generatedoperational environment information, which may include receiving theoperational environment information or a portion thereof.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may detect oridentify one or more distinct vehicle operational scenarios, such asbased on the operational environment represented by the operationalenvironment information, which may include the operational environmentinformation output by the operational environment monitors, theprobability of availability information output by the blocking monitor,or a combination thereof. For example, in accordance with the firsttemporal location, the autonomous vehicle operational managementcontroller may detect or identify one or more of a first pedestrianscenario including the first pedestrian 7400, a second pedestrianscenario including the second pedestrian 7410, a third pedestrianscenario including the third pedestrian 7420, and a fourth pedestrianscenario including the fourth pedestrian 7430.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may detect one ormore previously undetected vehicle operational scenarios. For example,in accordance with the first temporal location the autonomous vehicleoperational management controller may detect the first vehicleoperational scenario and in accordance with a second temporal locationfrom the sequence of temporal locations, such as a temporal locationsubsequent to the first temporal location, the autonomous vehicleoperational management controller may detect the second vehicleoperational scenario.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may instantiate oneor more pedestrian-scenario-specific operational control evaluationmodule instances in response to detecting or identifying one or more ofthe first pedestrian scenario including the first pedestrian 7400, thesecond pedestrian scenario including the second pedestrian 7410, thethird pedestrian scenario including the third pedestrian 7420, or thefourth pedestrian scenario including the fourth pedestrian 7430.

For example, in accordance with the first temporal location, theautonomous vehicle operational management controller may detect thefirst pedestrian scenario including the first pedestrian 7400, maydetermine that a pedestrian-scenario-specific operational controlevaluation module corresponding to the first pedestrian scenario isavailable, and may instantiate a first pedestrian-scenario-specificoperational control evaluation module instance in response to detectingthe first pedestrian scenario including the first pedestrian 7400.

In another example, the autonomous vehicle operational managementcontroller may detect the first pedestrian scenario including the firstpedestrian 7400, determine that a pedestrian-scenario-specificoperational control evaluation module corresponding to the firstpedestrian scenario is unavailable, generate and solve apedestrian-scenario-specific operational control evaluation modulepedestrian-scenario-specific operational control evaluation modulecorresponding to the first pedestrian scenario, and instantiate aninstance of the pedestrian-scenario-specific operational controlevaluation module corresponding to the first pedestrian scenario inresponse to detecting the first pedestrian scenario including the firstpedestrian 7400.

In some embodiments, the autonomous vehicle operational managementcontroller may detect or identify one or more of the pedestrianscenarios substantially concurrently. For example, the autonomousvehicle operational management controller may detect or identify thesecond pedestrian scenario including the second pedestrian 7410 and thethird pedestrian scenario including the third pedestrian 7420substantially concurrently.

In some embodiments, the autonomous vehicle operational managementcontroller may instantiate two or more respective instances ofrespective pedestrian-scenario-specific operational control evaluationmodules substantially concurrently. For example, the autonomous vehicleoperational management controller may detect or identify the secondpedestrian scenario including the second pedestrian 7410 and the thirdpedestrian scenario including the third pedestrian 7420 substantiallyconcurrently, and may instantiate an instance of thepedestrian-scenario-specific operational control evaluation modulecorresponding to the second pedestrian scenario substantiallyconcurrently with instantiating an instance of thepedestrian-scenario-specific operational control evaluation modulecorresponding to the third pedestrian scenario.

In another example, the autonomous vehicle operational managementcontroller may detect or identify the second pedestrian scenarioincluding the first expected path 7530 for the second pedestrian 7410and a fifth pedestrian scenario including the second expected path 7532for the second pedestrian 7410 substantially concurrently, and mayinstantiate an instance of a pedestrian-scenario-specific operationalcontrol evaluation module corresponding to the second pedestrianscenario substantially concurrently with instantiating an instance of apedestrian-scenario-specific operational control evaluation modulecorresponding to the fifth pedestrian scenario.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may send, orotherwise make available, operational environment information, such asnew or updated operational environment information, to previouslyinstantiated, or operating, scenario-specific operational controlevaluation module instances.

Instantiating, or updating, a scenario-specific operational controlevaluation module instance may include providing the operationalenvironment information, or a portion thereof, such as the sensorinformation or the probabilities of availability, to the respectivescenario-specific operational control evaluation module instances, suchas by sending the operational environment information, or a portionthereof, to the respective scenario-specific operational controlevaluation module instances, or storing the operational environmentinformation, or a portion thereof, for access by the respectivescenario-specific operational control evaluation module instances.

At one or more temporal location, such as at each temporal location, therespective pedestrian-scenario-specific operational control evaluationmodule instances may receive, or otherwise access, the operationalenvironment information corresponding to the respective autonomousvehicle operational control scenarios. For example, in accordance withthe first temporal location, the first pedestrian-scenario-specificoperational control evaluation module instance may receive operationalenvironment information corresponding to the first pedestrian scenario,which may include the probability of availability information for thearea or portion of the vehicle transportation network proximate to thepoint of convergence between the expected path 7520 for the firstpedestrian 7400 and the expected path 7500, or the route 7510, for theautonomous vehicle 7100.

A pedestrian-scenario-specific operational control evaluation module maymodel a pedestrian scenario as including states representingspatiotemporal locations for the autonomous vehicle 7100, spatiotemporallocations for the respective pedestrian 7400, 7410, 7420, 7430, andcorresponding blocking probabilities. A pedestrian-scenario-specificoperational control evaluation module may model a pedestrian scenario asincluding actions such as ‘stop’ (or ‘wait’), ‘advance’, and ‘proceed’.A pedestrian-scenario-specific operational control evaluation module maymodel a pedestrian scenario as including state transition probabilitiesrepresenting probabilities that a respective pedestrian enters anexpected path of the autonomous vehicle, such as by traversing anexpected path associated with the respective pedestrian. The statetransition probabilities may be determined based on the operationalenvironment information. A pedestrian-scenario-specific operationalcontrol evaluation module may model a pedestrian scenario as includingnegative value rewards for violating traffic control regulations, andincluding a positive value reward for completing the pedestrianscenario.

At one or more temporal location, such as at each temporal location,each instantiated pedestrian-scenario-specific operational controlevaluation module instance may generate a respective candidate vehiclecontrol action, such as ‘stop’, ‘advance’, or ‘proceed’, based on therespective modeled scenario and the corresponding operationalenvironment information, and may output the respective candidate vehiclecontrol action to the autonomous vehicle operational managementcontroller, such as by sending the respective candidate vehicle controlaction to the autonomous vehicle operational management controller orstoring the respective candidate vehicle control action for access bythe autonomous vehicle operational management controller.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may receivecandidate vehicle control actions from the respective instantiatedpedestrian-scenario-specific operational control evaluation moduleinstances and may identify a vehicle control action based on thereceived candidate vehicle control actions for controlling theautonomous vehicle 7100 at the corresponding temporal location and maycontrol the autonomous vehicle to traverse the vehicle transportationnetwork, or a portion thereof, in accordance with the identified vehiclecontrol action.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may determinewhether one or more of the detected vehicle operational scenarios hasexpired and, in response to determining that a vehicle operationalscenarios has expired, may uninstantiate correspondingpedestrian-scenario-specific operational control evaluation moduleinstances.

FIG. 8 is a diagram of an example of an intersection scene 8000including intersection scenarios in accordance with embodiments of thisdisclosure. Autonomous vehicle operational management, such as theautonomous vehicle operational management 5000 shown in FIG. 5, mayinclude an autonomous vehicle 8100, such as the vehicle 1000 shown inFIG. 1, one of the vehicles 2100/2110 shown in FIG. 2, a semi-autonomousvehicle, or any other vehicle implementing autonomous driving, operatingan autonomous vehicle operational management system, such as theautonomous vehicle operational management system 4000 shown in FIG. 4,including an intersection-scenario-specific operational controlevaluation module instance, which may be an instance of anintersection-scenario-specific operational control evaluation module,such as the intersection-scenario-specific operational controlevaluation module 4420 shown in FIG. 4, which may be a model of anautonomous vehicle operational control scenario that includes theautonomous vehicle 8100 traversing a portion of the vehicletransportation network including an intersection. For simplicity andclarity, the portion of the vehicle transportation network correspondingto the intersection scene 8000 shown in FIG. 8 is oriented with north atthe top and east at the right.

The portion of the vehicle transportation network corresponding to theintersection scene 8000 shown in FIG. 8 includes the autonomous vehicle8100 traversing a first road 8200 from west to east, approaching anintersection 8210 with a second road 8220. An expected path 8110 for theautonomous vehicle 8100 indicates that the autonomous vehicle 8100 maytraverse straight through the intersection 8210. A first alternativeexpected path 8120 for the autonomous vehicle 8100, shown using a brokenline, indicates that the autonomous vehicle 8100 may traverse theintersection 8210 by turning right from the first road 8200 to thesecond road 8220. A second alternative expected path 8130 for theautonomous vehicle 8100, shown using a broken line, indicates that theautonomous vehicle 8100 may traverse the intersection 8210 by turningleft from the first road 8200 to the second road 8220.

A first remote vehicle 8300 is shown traversing south along a firstsouthbound lane the second road 8220 approaching the intersection 8210.A second remote vehicle 8310 is shown traversing north along a firstnorthbound lane of the second road 8220 approaching the intersection8210. A third remote vehicle 8320 is shown traversing north along asecond northbound lane of the second road 8220 approaching theintersection 8210. A fourth remote vehicle 8330 is shown traversingnorth along the first northbound lane of the second road 8220approaching the intersection 8210.

The autonomous vehicle operational management system may include anautonomous vehicle operational management controller, such as theautonomous vehicle operational management controller 4100 shown in FIG.4 or the executor 5100 shown in FIG. 5, and a blocking monitor, such asthe blocking monitor 4200 shown in FIG. 4 or the blocking monitor 5200shown in FIG. 5. The autonomous vehicle 8100 may include one or moresensors, one or more operational environment monitors, or a combinationthereof.

In some embodiments, the autonomous vehicle operational managementsystem may operate continuously or periodically, such as at eachtemporal location in a sequence of temporal locations. For simplicityand clarity, the geospatial location of the autonomous vehicle 8100, thefirst remote vehicle 8300, the second remote vehicle 8310, the thirdremote vehicle 8320, and the fourth remote vehicle 8330 is shown inaccordance with a first, sequentially earliest, temporal location fromthe sequence of temporal locations. Although described with reference toa sequence of temporal locations for simplicity and clarity, each unitof the autonomous vehicle operational management system may operate atany frequency, the operation of respective units may be synchronized orunsynchronized, and operations may be performed concurrently with one ormore portions of one or more temporal locations. For simplicity andclarity, respective descriptions of one or more temporal locations, suchas temporal locations between the temporal locations described herein,may be omitted from this disclosure.

At one or more temporal location, such as at each temporal location, thesensors of the autonomous vehicle 8100 may detect informationcorresponding to the operational environment of the autonomous vehicle8100, such as information corresponding to one or more of the remotevehicles 8300, 8310, 8320, 8330.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management system may identify anexpected path 8110, 8120, 8130 for the autonomous vehicle 8100, a route(not shown) for the autonomous vehicle 8100, or both.

At one or more temporal location, such as at each temporal location, theoperational environment monitors of the autonomous vehicle 8100 mayidentify or generate operational environment information representing anoperational environment, or an aspect thereof, of the autonomous vehicle8100, such as in response to receiving sensor information correspondingto the remote vehicles 8300, 8310, 8320, 8330, which may includeassociating the sensor information with the remote vehicles 8300, 8310,8320, 8330, and may output the operational environment information,which may include information representing the remote vehicles 8300,8310, 8320, 8330, to the autonomous vehicle operational managementcontroller.

At one or more temporal location, such as at each temporal location, theblocking monitor may generate probability of availability informationindicating respective probabilities of availability for one or moreareas or portions of the vehicle transportation network. For example,the blocking monitor may determine one or more probable expected paths8400, 8402 for the first remote vehicle 8300, one or more probableexpected paths 8410, 8412 for the second remote vehicle 8310, one ormore probable expected paths 8420, 8422 for the third remote vehicle8320, and an expected path 8430 for the fourth remote vehicle 8330. Theblocking monitor may generate probability of availability informationindicating respective probabilities of availability for one or moreareas or portions of the vehicle transportation network corresponding toone or more of the expected path 8110 for the autonomous vehicle 8100,the first alternative expected path 8120 for the autonomous vehicle8100, or the second alternative expected path 8130 for the autonomousvehicle 8100.

In some embodiments, generating the probability of availabilityinformation may include generating probabilities of availability for arespective area or portion of the vehicle transportation networkcorresponding to multiple temporal locations from the sequence oftemporal locations. The blocking monitor may output the probability ofavailability information to, or for access by, the autonomous vehicleoperational management controller.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may generateoperational environment information, or update previously generatedoperational environment information, which may include receiving theoperational environment information or a portion thereof.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may detect oridentify one or more distinct vehicle operational scenarios, such asbased on the operational environment represented by the operationalenvironment information, which may include the operational environmentinformation output by the operational environment monitors, theprobability of availability information output by the blocking monitor,or a combination thereof. For example, the autonomous vehicleoperational management controller may detect or identify one or more ofa first intersection scenario including the first remote vehicle 8300, asecond intersection scenario including the second remote vehicle 8310, athird intersection scenario including the third remote vehicle 8320, anda fourth intersection scenario including the fourth remote vehicle 8330.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may detect one ormore previously undetected vehicle operational scenarios. For example,in accordance with a first temporal location the autonomous vehicleoperational management controller may detect the first intersectionscenario and in accordance with a second temporal location from thesequence of temporal locations, such as a temporal location subsequentto the first temporal location, the autonomous vehicle operationalmanagement controller may detect the second intersection scenario.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may instantiate oneor more intersection-scenario-specific operational control evaluationmodule instances in response to detecting or identifying one or more ofthe first intersection scenario, the second intersection scenario, thethird intersection scenario, or the fourth intersection scenario.

In some embodiments, the autonomous vehicle operational managementcontroller may detect or identify one or more of the intersectionscenarios substantially concurrently. For example, the autonomousvehicle operational management controller may detect or identify thesecond intersection scenario and the third intersection scenariosubstantially concurrently.

In some embodiments, the autonomous vehicle operational managementcontroller may instantiate two or more respective instances ofrespective intersection-scenario-specific operational control evaluationmodules substantially concurrently. For example, the autonomous vehicleoperational management controller may detect or identify the secondintersection scenario and the third intersection scenario substantiallyconcurrently, and may instantiate an instance of theintersection-scenario-specific operational control evaluation modulecorresponding to the second intersection scenario substantiallyconcurrently with instantiating an instance of theintersection-scenario-specific operational control evaluation modulecorresponding to the third intersection scenario.

In another example, the autonomous vehicle operational managementcontroller may detect or identify the second intersection scenarioincluding the first expected path 8400 for the first remote vehicle 8300and a fifth intersection scenario including the second expected path8402 for the first remote vehicle 8300 substantially concurrently, andmay instantiate an instance of an intersection-scenario-specificoperational control evaluation module corresponding to the secondintersection scenario substantially concurrently with instantiating aninstance of an intersection-scenario-specific operational controlevaluation module corresponding to the fifth intersection scenario.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may send, orotherwise make available, operational environment information, such asnew or updated operational environment information, to previouslyinstantiated, or operating, scenario-specific operational controlevaluation module instances.

Instantiating, or updating, a scenario-specific operational controlevaluation module instance may include providing the operationalenvironment information, or a portion thereof, such as the sensorinformation or the probabilities of availability, to the respectivescenario-specific operational control evaluation module instances, suchas by sending the operational environment information, or a portionthereof, to the respective scenario-specific operational controlevaluation module instances, or storing the operational environmentinformation, or a portion thereof, for access by the respectivescenario-specific operational control evaluation module instances.

In some embodiments, the operational environment information mayindicate operational information for the autonomous vehicle 8100, suchas geospatial location information, velocity information, accelerationinformation, pendency information, priority information, or acombination thereof, and operational information for one or more of theremote vehicles 8300, 8310, 8320, 8330, such as geospatial locationinformation, velocity information, acceleration information, pendencyinformation, priority information, or a combination thereof. Thependency information may indicate a temporal period corresponding to therespective vehicle and a respective geographic location, such a periodof time that the respective vehicle has been stationary at theintersection. The priority information may indicate a right-of-waypriority corresponding to a respective vehicle relative to othervehicles in the intersection scene 8000.

An intersection-scenario-specific operational control evaluation modulemay model an intersection scenario as including states representingspatiotemporal locations for the autonomous vehicle 8100, spatiotemporallocations for the respective remote vehicles 8300, 8310, 8320, 8330,pendency information, priority information, and corresponding blockingprobabilities. An intersection-scenario-specific operational controlevaluation module may model an intersection scenario as includingactions such as ‘stop’ (or ‘wait’), ‘advance’, and ‘proceed’. Anintersection-scenario-specific operational control evaluation module maymodel an intersection scenario as including state transitionprobabilities representing probabilities that a respective intersectionenters an expected path of the autonomous vehicle, such as by traversingan expected path associated with the respective intersection. The statetransition probabilities may be determined based on the operationalenvironment information. An intersection-scenario-specific operationalcontrol evaluation module may model an intersection scenario asincluding negative value rewards for violating traffic controlregulations, and including a positive value reward for completing theintersection scenario.

At one or more temporal location, such as at each temporal location, therespective intersection-scenario-specific operational control evaluationmodule instances may receive, or otherwise access, the operationalenvironment information corresponding to the respective intersectionscenarios. For example, in accordance with the first temporal location,the first intersection-scenario-specific operational control evaluationmodule instance may receive operational environment informationcorresponding to the first intersection scenario, which may include theprobability of availability information for the area or portion of thevehicle transportation network proximate to the point of convergencebetween the first expected path 8400 for the first remote vehicle 8300and the expected path 8110 for the autonomous vehicle 8100.

At one or more temporal location, such as at each temporal location,each instantiated intersection-scenario-specific operational controlevaluation module instance may generate a respective candidate vehiclecontrol action, such as ‘stop’, ‘advance’, or ‘proceed’, based on therespective modeled scenario and the corresponding operationalenvironment information, and may output the respective candidate vehiclecontrol action to the autonomous vehicle operational managementcontroller, such as by sending the respective candidate vehicle controlaction to the autonomous vehicle operational management controller orstoring the respective candidate vehicle control action for access bythe autonomous vehicle operational management controller.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may receivecandidate vehicle control actions from the respective instantiatedintersection-scenario-specific operational control evaluation moduleinstances and may identify a vehicle control action based on thereceived candidate vehicle control actions for controlling theautonomous vehicle 8100 at the corresponding temporal location and maycontrol the autonomous vehicle 8100 to traverse the vehicletransportation network, or a portion thereof, in accordance with theidentified vehicle control action.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may determinewhether one or more of the detected intersection scenarios has expiredand, in response to determining that an intersection scenario hasexpired, may uninstantiate corresponding intersection-scenario-specificoperational control evaluation module instances.

FIG. 9 is a diagram of an example of a lane change scene 9000 includinga lane change scenario in accordance with embodiments of thisdisclosure. Autonomous vehicle operational management, such as theautonomous vehicle operational management 5000 shown in FIG. 5, mayinclude an autonomous vehicle 9100, such as the vehicle 1000 shown inFIG. 1, one of the vehicles 2100, 2110 shown in FIG. 2, asemi-autonomous vehicle, or any other vehicle implementing autonomousdriving, operating an autonomous vehicle operational management system,such as the autonomous vehicle operational management system 4000 shownin FIG. 4, including a lane change-scenario-specific operational controlevaluation module instance, which may be an instance of a lanechange-scenario-specific operational control evaluation module, such asthe lane change-scenario-specific operational control evaluation module4430 shown in FIG. 4, which may be a model of an autonomous vehicleoperational control scenario that includes the autonomous vehicle 9100traversing a portion of the vehicle transportation network by performinga lane change. For simplicity and clarity, the portion of the vehicletransportation network corresponding to the lane change scene 9000 shownin FIG. 9 is oriented with north at the top and east at the right.

The portion of the vehicle transportation network corresponding to thelane change scene 9000 shown in FIG. 9 includes the autonomous vehicle9100 traversing northbound along a first road 9200. The first road 9200include an eastern northbound lane 9210 and a western northbound lane9220. A current expected path 9110 for the autonomous vehicle 9100indicates that the autonomous vehicle 9100 is traveling northbound inthe eastern northbound lane 9210. An alternative expected path 9120 forthe autonomous vehicle 9100, shown using a broken line, indicates thatthe autonomous vehicle 9100 may traverse the vehicle transportationnetwork by performing a lane change from the eastern northbound lane9210 to the western northbound lane 9220.

A first remote vehicle 9300 is shown traversing northbound along theeastern northbound lane 9210 ahead (north) of the autonomous vehicle9100. A second remote vehicle 9400 is shown traversing northbound alongthe western northbound lane 9220 behind (south) of the autonomousvehicle 9100.

The autonomous vehicle operational management system may include anautonomous vehicle operational management controller, such as theautonomous vehicle operational management controller 4100 shown in FIG.4 or the executor 5100 shown in FIG. 5, and a blocking monitor, such asthe blocking monitor 4200 shown in FIG. 4 or the blocking monitor 5200shown in FIG. 5. The autonomous vehicle 9100 may include one or moresensors, one or more operational environment monitors, or a combinationthereof.

In some embodiments, the autonomous vehicle operational managementsystem may operate continuously or periodically, such as at eachtemporal location in a sequence of temporal locations. For simplicityand clarity, the geospatial location of the autonomous vehicle 9100, thefirst remote vehicle 9300, and the second remote vehicle 9400 is shownin accordance with a first, sequentially earliest, temporal locationfrom the sequence of temporal locations. Although described withreference to a sequence of temporal locations for simplicity andclarity, each unit of the autonomous vehicle operational managementsystem may operate at any frequency, the operation of respective unitsmay be synchronized or unsynchronized, and operations may be performedconcurrently with one or more portions of one or more temporallocations. For simplicity and clarity, respective descriptions of one ormore temporal locations, such as temporal locations between the temporallocations described herein, may be omitted from this disclosure.

At one or more temporal location, such as at each temporal location, thesensors of the autonomous vehicle 9100 may detect informationcorresponding to the operational environment of the autonomous vehicle9100, such as information corresponding to one or more of the remotevehicles 9300, 9400.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management system may identify anexpected path 9110, 9120 for the autonomous vehicle 9100, a route (notshown) for the autonomous vehicle 9100, or both.

At one or more temporal location, such as at each temporal location, theoperational environment monitors of the autonomous vehicle 9100 mayidentify or generate operational environment information representing anoperational environment, or an aspect thereof, of the autonomous vehicle9100, such as in response to receiving sensor information correspondingto the remote vehicles 9300, 9400, which may include associating thesensor information with the remote vehicles 9300, 9400, and may outputthe operational environment information, which may include informationrepresenting the remote vehicles 9300, 9400, to the autonomous vehicleoperational management controller.

At one or more temporal location, such as at each temporal location, theblocking monitor may generate probability of availability informationindicating respective probabilities of availability for one or moreareas or portions of the vehicle transportation network. For example,the blocking monitor may determine one or more probable expected paths9310, 9320 for the first remote vehicle 9300, and one or more probableexpected paths 9410, 9420 for the second remote vehicle 9400. The firstprobable expected path 9310 for the first remote vehicle 9300 indicatesthat the first remote vehicle 9300 traverses the corresponding portionof the vehicle transportation network in the eastern northbound lane9210. The second probable expected path 9320, shown using a broken line,for the first remote vehicle 9300 indicates that the first remotevehicle 9300 traverses the corresponding portion of the vehicletransportation network by performing a lane change into the westernnorthbound lane 9220. The first probable expected path 9410 for thesecond remote vehicle 9400 indicates that the second remote vehicle 9400traverses the corresponding portion of the vehicle transportationnetwork in the western northbound lane 9220. The second probableexpected path 9420, shown using a broken line, for the second remotevehicle 9400 indicates that the second remote vehicle 9400 traverses thecorresponding portion of the vehicle transportation network byperforming a lane change into the eastern northbound lane 9210.

The blocking monitor may generate probability of availabilityinformation indicating respective probabilities of availability for oneor more areas or portions of the vehicle transportation networkcorresponding to one or more of the expected path 9110 for theautonomous vehicle 9100, or the alternate expected path 9120 for theautonomous vehicle 9100.

In some embodiments, generating the probability of availabilityinformation may include generating probabilities of availability for arespective area or portion of the vehicle transportation networkcorresponding to multiple temporal locations from the sequence oftemporal locations. The blocking monitor may output the probability ofavailability information to, or for access by, the autonomous vehicleoperational management controller.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may generateoperational environment information, or update previously generatedoperational environment information, which may include receiving theoperational environment information or a portion thereof.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may detect oridentify one or more distinct vehicle operational scenarios, such asbased on the operational environment represented by the operationalenvironment information, which may include the operational environmentinformation output by the operational environment monitors, theprobability of availability information output by the blocking monitor,or a combination thereof. For example, the autonomous vehicleoperational management controller may detect or identify one or more ofa first lane change scenario including the first remote vehicle 9300, asecond lane change scenario including the second remote vehicle 9400, orboth.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may instantiate oneor more lane change-scenario-specific operational control evaluationmodule instances in response to detecting or identifying one or more ofthe first lane change scenario or the second lane change scenario.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may send, orotherwise make available, operational environment information, such asnew or updated operational environment information, to previouslyinstantiated, or operating, scenario-specific operational controlevaluation module instances.

Instantiating, or updating, a scenario-specific operational controlevaluation module instance may include providing the operationalenvironment information, or a portion thereof, such as the sensorinformation or the probabilities of availability, to the respectivescenario-specific operational control evaluation module instances, suchas by sending the operational environment information, or a portionthereof, to the respective scenario-specific operational controlevaluation module instances, or storing the operational environmentinformation, or a portion thereof, for access by the respectivescenario-specific operational control evaluation module instances.

In some embodiments, the operational environment information mayindicate operational information for the autonomous vehicle 9100, suchas geospatial location information, velocity information, accelerationinformation, or a combination thereof, and operational information forone or more of the remote vehicles 9300, 9400, such as geospatiallocation information, velocity information, acceleration information, ora combination thereof.

A lane change-scenario-specific operational control evaluation modulemay model a lane change scenario as including states representingspatiotemporal locations for the autonomous vehicle 9100, spatiotemporallocations for the respective remote vehicles 9300, 9400, andcorresponding blocking probabilities. A lane change-scenario-specificoperational control evaluation module may model a lane change scenarioas including actions such as ‘maintain’, ‘accelerate’, ‘decelerate’, and‘proceed’ (change lanes). A lane change-scenario-specific operationalcontrol evaluation module may model a lane change scenario as includingstate transition probabilities representing probabilities that arespective remote vehicle 9300, 9400 enters an expected path 9110, 9120of the autonomous vehicle 9100. For example, the first remote vehicle9300 may enter the alternate expected path 9120 of the autonomousvehicle 9100 by traversing the alternate expected path 9320 for thefirst remote vehicle 9300 at a velocity less than a velocity of theautonomous vehicle 9100. In another example, the second remote vehicle9400 may enter the alternate expected path 9120 of the autonomousvehicle 9100 by traversing the expected path 9410 for the second remotevehicle 9400 at a velocity greater than the velocity of the autonomousvehicle 9100. The state transition probabilities may be determined basedon the operational environment information. A lanechange-scenario-specific operational control evaluation module may modela lane change scenario as including negative value rewards for violatingtraffic control regulations, and including a positive value reward forcompleting the lane change scenario.

At one or more temporal location, such as at each temporal location, therespective lane change-scenario-specific operational control evaluationmodule instances may receive, or otherwise access, the operationalenvironment information corresponding to the respective lane changescenarios. For example, the second lane change-scenario-specificoperational control evaluation module instance may receive operationalenvironment information corresponding to the second lane changescenario, which may include the probability of availability informationfor the area or portion of the vehicle transportation network proximateto the point of convergence between the expected path 9410 for thesecond remote vehicle 9400 and the alternate expected path 9120 for theautonomous vehicle 9100.

At one or more temporal location, such as at each temporal location,each instantiated lane change-scenario-specific operational controlevaluation module instance may generate a respective candidate vehiclecontrol action, such as ‘maintain’, ‘accelerate’, ‘decelerate’, or‘proceed’, based on the respective modeled scenario and thecorresponding operational environment information, and may output therespective candidate vehicle control action to the autonomous vehicleoperational management controller, such as by sending the respectivecandidate vehicle control action to the autonomous vehicle operationalmanagement controller or storing the respective candidate vehiclecontrol action for access by the autonomous vehicle operationalmanagement controller.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may receivecandidate vehicle control actions from the respective instantiated lanechange-scenario-specific operational control evaluation module instancesand may identify a vehicle control action based on the receivedcandidate vehicle control actions for controlling the autonomous vehicle9100 at the corresponding temporal location and may control theautonomous vehicle 9100 to traverse the vehicle transportation network,or a portion thereof, in accordance with the identified vehicle controlaction.

At one or more temporal location, such as at each temporal location, theautonomous vehicle operational management controller may determinewhether one or more of the detected lane change scenarios has expiredand, in response to determining that a lane change scenario has expired,may uninstantiate corresponding lane change-scenario-specificoperational control evaluation module instances.

The above-described aspects, examples, and implementations have beendescribed in order to allow easy understanding of the disclosure are notlimiting. On the contrary, the disclosure covers various modificationsand equivalent arrangements included within the scope of the appendedclaims, which scope is to be accorded the broadest interpretation so asto encompass all such modifications and equivalent structure as ispermitted under the law.

What is claimed is:
 1. A method for use in traversing a vehicletransportation network, the method comprising: traversing, by anautonomous vehicle, a vehicle transportation network, wherein traversingthe vehicle transportation network includes: operating ascenario-specific operational control evaluation module instance,wherein the scenario-specific operational control evaluation moduleinstance is an instance of a scenario-specific operational controlevaluation module, wherein the scenario-specific operational controlevaluation module implements a partially observable Markov decisionprocess; receiving a candidate vehicle control action from thescenario-specific operational control evaluation module instance; andtraversing a portion of the vehicle transportation network based on thecandidate vehicle control action.
 2. The method of claim 1, whereintraversing the portion of the vehicle transportation network includestraversing the portion of the vehicle transportation network inaccordance with an identified route.
 3. The method of claim 1, whereinthe scenario-specific operational control evaluation module instance isassociated with an external object within a defined geospatial distancefrom the autonomous vehicle.
 4. The method of claim 1, wherein thescenario-specific operational control evaluation module instance is aninstance of a scenario-specific operational control evaluation modulefrom a plurality of scenario-specific operational control evaluationmodules.
 5. The method of claim 4, wherein each scenario-specificoperational control evaluation module from the plurality ofscenario-specific operational control evaluation modules models adistinct vehicle operational scenario.
 6. The method of claim 1, whereinoperating the scenario-specific operational control evaluation moduleinstance includes modeling, by the scenario-specific operational controlevaluation module instance, a distinct vehicle operational scenario. 7.The method of claim 6, wherein modeling the distinct vehicle operationalscenario includes modeling sensor uncertainty.
 8. The method of claim 6,wherein modeling the distinct vehicle operational control scenarioincludes: receiving sensor information from a sensor of the autonomousvehicle; and modeling the distinct vehicle operational control scenariobased on the sensor information.
 9. The method of claim 8, whereinreceiving the sensor information includes: receiving processed sensorinformation from a sensor information processing unit of the autonomousvehicle, the sensor information processing unit receiving the sensorinformation from the sensor of the autonomous vehicle and generating theprocessed sensor information based on the sensor information.
 10. Themethod of claim 8, wherein modeling the distinct vehicle operationalcontrol scenario includes: receiving vehicle transportation networkinformation representing the vehicle transportation network; andmodeling the distinct vehicle operational control scenario based on thesensor information and the vehicle transportation network information.11. The method of claim 6, wherein modeling the distinct vehicleoperational control scenario includes: receiving a probability ofavailability for the portion of the vehicle transportation network froma blocking monitor instance; and modeling the distinct vehicleoperational control scenario based on the probability of availability.12. The method of claim 11, wherein modeling the distinct vehicleoperational control scenario includes: identifying a plurality ofstates; identifying a plurality of vehicle control actions; identifyinga plurality of conditional transition probabilities, wherein eachconditional transition probability from the plurality of conditionaltransition probabilities represents a probability of transitioning froma first respective state from the plurality of states to a secondrespective state from the plurality of states; identifying a rewardfunction, wherein the reward function generates a reward correspondingto transitioning from a first respective state from the plurality ofstates to a second respective state from the plurality of states;identifying a plurality of observations, wherein the probability ofavailability is one of the plurality of observations, and wherein thesensor information includes one or more of the probability ofobservations; and identifying a plurality of conditional observationprobabilities, wherein each conditional observation probability from theplurality of conditional observation probabilities indicates aprobability of accuracy for a respective observation from the pluralityof observations.
 13. The method of claim 12, wherein the plurality ofstates includes one or more of: an indication of a current geospatiallocation of the autonomous vehicle; an indication of a kinematic stateof the autonomous vehicle; an indication of a temporal period of theautonomous vehicle at the current geospatial location; an indication ofa current geospatial location of the external object; an indication of akinematic state of the external object; an indication of a probabilityof availability corresponding to the external object; or an indicationof a priority for the autonomous vehicle relative to the externalobject.
 14. The method of claim 12, wherein the plurality of vehiclecontrol actions includes one or more of: a stop vehicle control action;a wait vehicle control action; an accelerate vehicle control action; adecelerate vehicle control action; an advance vehicle control action; ora proceed vehicle control action.
 15. The method of claim 12, wherein,on a condition that transitioning from the first respective state fromthe plurality of states to the second respective state from theplurality of states includes violating a vehicle transportation networkregulation, the reward function generates a penalty.
 16. The method ofclaim 12, wherein, on a condition that the scenario-specific operationalcontrol evaluation module that implements the partially observableMarkov decision process is unavailable, traversing the vehicletransportation network includes: operating a scenario-specificoperational control evaluation module instance that is an instance of ascenario-specific operational control evaluation module that implementsa Markov decision process.
 17. The method of claim 16, wherein operatingthe scenario-specific operational control evaluation module instancethat is the instance of the scenario-specific operational controlevaluation module that implements the Markov decision process includes:generating the scenario-specific operational control evaluation module;solving the scenario-specific operational control evaluation module; andinstantiating the scenario-specific operational control evaluationmodule instance.
 18. A method for use in traversing a vehicletransportation network, the method comprising: traversing, by anautonomous vehicle, a vehicle transportation network, wherein traversingthe vehicle transportation network includes: instantiating ascenario-specific operational control evaluation module instance that isan instance of a scenario-specific operational control evaluation modulethat implements a partially observable Markov decision process modelinga distinct vehicle operational control scenario, wherein modeling thedistinct vehicle operational control scenario includes: identifying aplurality of states corresponding to the distinct vehicle operationalcontrol scenario; identifying a plurality of available vehicle controlactions based on the distinct vehicle operational control scenario;identifying a plurality of conditional transition probabilities, whereineach conditional transition probability from the plurality ofconditional transition probabilities represents a probability oftransitioning from a first respective state from the plurality of statesto a second respective state from the plurality of states; identifying areward function, wherein the reward function generates a rewardcorresponding to transitioning from a first respective state from theplurality of states to a second respective state from the plurality ofstates; identifying a plurality of observations, each observation fromthe plurality of observations corresponding to a respective state fromthe plurality of states; and identifying a plurality of conditionalobservation probabilities, wherein each conditional observationprobability from the plurality of conditional observation probabilitiesindicates a probability of accuracy for a respective observation fromthe plurality of observations; receiving a candidate vehicle controlaction from the scenario-specific operational control evaluation moduleinstance; and traversing a portion of the vehicle transportation networkbased on the candidate vehicle control action.
 19. An autonomous vehiclecomprising: a processor configured to execute instructions stored on anon-transitory computer readable medium to: operate a scenario-specificoperational control evaluation module instance, wherein thescenario-specific operational control evaluation module instance is aninstance of a scenario-specific operational control evaluation module,wherein the scenario-specific operational control evaluation moduleimplements a partially observable Markov decision process; receive acandidate vehicle control action from the scenario-specific operationalcontrol evaluation module instance; and control the autonomous vehicleto traverse a portion of the vehicle transportation network based on thecandidate vehicle control action.
 20. The autonomous vehicle of claim19, wherein the processor is configured to execute the instructionsstored on the non-transitory computer readable medium to operate thescenario-specific operational control evaluation module instance to:identify a plurality of states for a distinct vehicle operationalscenario; identify a plurality of vehicle control actions for thedistinct vehicle operational scenario; identify a plurality ofconditional transition probabilities, wherein each conditionaltransition probability from the plurality of conditional transitionprobabilities represents a probability of transitioning from a firstrespective state from the plurality of states to a second respectivestate from the plurality of states; identify a reward function, whereinthe reward function generates a reward corresponding to transitioningfrom a first respective state from the plurality of states to a secondrespective state from the plurality of states; identify a plurality ofobservations, each observation from the plurality of observationscorresponding to a respective state from the plurality of states; andidentify a plurality of conditional observation probabilities, whereineach conditional observation probability from the plurality ofconditional observation probabilities indicates a probability ofaccuracy for a respective observation from the plurality ofobservations.